Towards an architecture for universities management
Assisting students in the choice of their specialization
Inaya Lahoud1, Fatma Chamekh2
1
Dept. of Computer Science, University of Galatasaray, Istanbul, Turkey
clahoud@gsu.edu.tr
2University of Lyon, Lyon, France
Fatma.chamekh@univ-lyon3.fr
ABSTRACT. The heterogeneity and the high volume of data on the Web are the
main features that make it a promoter field of knowledge engineering for re-
searcher. However, the user is getting lost towards the diversity of information
on the Web. In this paper, our aim is to propose an approach to assist user. Our
approach uses Semantic Web technologies. Our scenario is focused on the field
of education and in particular higher education. This choice is motivated by the
diversity of information sources where the student is dispersed.
Keywords: Ontologies; High education institution; universities management
system
1 Introduction
Nowadays, the web offers advanced interactions between users and data providers.
Since the open data initiative, the data on the Web has increased. Many institutions
have published their data in heterogeneous and distributed way. The universities and
educational institutions follow this way by providing a data concerning their training
offers. Users retrieve and review educational training. The main idea is to share their
experiences and get the best feedback about universities.
Our Objective is to match the user’s needs and trainings offered by institutions. The
management of the data from diverse resources is complex and tedious process, relying
mainly on human-based and error-prone tasks. Globally, there is a lack of practical
approach for converting and linking multi-origin data pieces into one coherent smart
data set. More specifically, the following scientific locks make the transformation of
data on a smart data is difficult task. For this end we present the main challenges behind
our research work:
Data usually comes in variable quality unorganized or not described, and not linked
to other sources on the Web. There is a need to homogenize vocabularies by provid-
ing machine-readable explicit description of data semantics, and linking data sets to
each other and to ontologies.
7
The combination of heterogeneous data from different sources generates some is-
sues. Those one could be classified on three levels: the syntactic level related to data
format and syntactic, the semantic level when different knowledge presentations are
used and the structural level due to the different data organizations.
We focused in this paper on assisting users in their information research by offering
them a universities research system. This system seeks the most appropriate training
for a student according to his criteria (university ranking, domain of training, geography
of university and unemployment percent). In addition, this system offers to him train-
ings where he can benefit from social or excellence scholarships, grants from the state
to encourage certain areas, or simply where the unemployment rate is lowest and there-
fore he has more chances to acquire employment at the end of his studies.
Our system uses the Open Data for higher education and unemployment available at
data.gouv.fr.
The remainder of this paper is organized as follows: section 2 states our problem,
section 3 presents some existing approach that use semantic web technologies for edu-
cation. Section 4 depicts our framework. Future works and conclusion are presented in
section 5.
2 Problem statement
We conducted our study in the field of education and more specifically on higher edu-
cation institutions. Indeed, the number of higher education institutions is increasing
every year. If we count the number of these institutions in a single country such as
France (more than 35001) and the United States (more than 4500 [1]), and the number
of training courses that offer these institutions, we can understand why students are
always lost in the choice of their studies.
The final year of high school is the year when students accrue the stress of the pas-
sage of the high school diploma, the daily difficulties and the choice of their future
orientation.
Most students, who have managed to continue their studies, think that they were insuf-
ficiently or badly informed and advised and therefore they chose the training in which
they can succeed. Not having a clear and precise idea of the job they wish to exercise,
nor a long-term professional objective, these students eventually chose more often the
curriculum that leaves most open doors. Nevertheless, the difficulties are not especially
evacuated for those who are still looking for jobs, then they wonder about their chances
to integrate quickly the job market and are asking questions about the appropriateness
of their courses’ choice [2].
In front of this situation, we need to propose to the student a system to manage the
huge and heterogeneous amount of data by taking into account the student criteria. Our
general challenge is summarized as follows: given the data provided by educational
institutions, universities, government and companies, whose understandability remains
difficult, semantic web technologies could provide an efficient solution.
1 education.gouv.fr
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3 Related work
The exploitation and the search for information on the Web become increasingly com-
plicated task. How to find information that we seek quickly seen the diversity and the
quantity of data on the Web? Where to search this information since there is no global
database that stores all data of the whole world as wished Tim Berners Lee? What is
the relevance of the information we found on the Web? Do they fully correspond to our
research?
There are many researchers working on these issues and try to find solutions. As we
explained in the introduction, we focused our study on the education and specifically
on higher education institutions. Our goal is to consolidate information of higher edu-
cation in one system where students can find what training is most appropriate for them
basing on university ranking, percentage of employment in this field, geography, and
available scholarships.
Semantic web technologies are getting increasingly used in various contexts, higher
education is no exception. Different approaches are designed to spread the services of
the universities which are distributed across several departments to serve their substan-
tial student base. In their survey, Dietze et al [3] highlight the growing use of linked
data by various universities. Many platforms are made available for direct consumption
and reuse. This includes for example the OU’s linked open data platform
(http://data.open.ac.uk), the University of Muenster (http://data.uni-muenster.de), the
University of Southampton (http://data.southampton.ac.uk) among others. The data
available through those platforms include: Courses information podcasts, Library cata-
logue, research publications, OpenLearn, reading experience database, the open arts
archive events, information about university staff and buildings located across the UK.
Besides that, the data of the platforms cited above are connected to the linked open data
cloud. Dedicated graphs include links to the Dbpedia entities, Geonames entities and
BBC entities. The external entities have the topics of media objects, web pages, courses.
The last few years, we have seen several websites style MOOCs such as Coursera2
FUN3. These websites allow users a free access to online courses. However, the aim of
our system is to search trainings offered by universities and not online courses as the
MOOC case.
[4] [5] [6] have proposed methodologies and frameworks to transform the existing
data sources into linked data. The main idea is to turn the available organizational data
in a linked data cloud, using pre-programmed transformation pattern. To follow the
success of social and knowledge graphs functionalities provided by facebook 4 and
google5, Health et al [7] proposed to create an education graph by processing courses
information and learning material from various universities in UK. They rely on bibli-
2 https://www.coursera.org/
3 https://www.fun-mooc.fr/
4 http://newsroom.fb.com/News/562/Introducing-Graph-Search-Beta
5 http://www.google.com/insidesearch/features/search/knowledge.html
9
ographical data of material repositories to identify links to course resource. In this di-
rection, the LinkedUpDataCatalog6 or related community initiatives7 are initial efforts
to collect and catalog dataset have been made by universities or other institutions.
Zablith [8] created a semantic data layer to conceptually connect courses taught in a
higher education program. The aim is to interlink courses within the same institution at
the level of concepts covered in course topics. For this reason, the author proposed a
courses information data model.
The limitations of approaches explained above are mainly related to the following
aspects. First, the data provided by those platforms are mainly limited to the educational
information such as courses program, publication. That means student can find the
courses topics and courses material or web pages. Also, those dataset are related to
external dataset such as Dbpedia or geonames but to enrich the educational data. Sec-
ond, the first efforts to connect various datasets are proposed by [7] but it is limited to
universities of UK.
In the other side we didn’t find researches who occur the subject of trainings’ clas-
sification to obtain scholarships except Rad [9] who presented in his paper a study in
Iran on the classification of university courses using data mining techniques. These
courses were classified according to different criteria to determine which training is the
most promising in the future. The Iranian state uses the result of this classification then
to invest in these trainings and pro-vide scholarships to encourage students to follow
them.
In our context, the aim is to assist the student to select a training. This selection
should be made following various criteria such age the university, the rank, scholarships
existence, and the unemployment average. To our knowledge, there is no existing ap-
proach that covers these requirements. To achieve our objective, we have to connect
the educational and government datasets. We present in the next section our approach
of universities management system in order to assist students in their choice.
4 Global approach
The idea of our work is to deal with academic and government data and make them
more accessible to users. Our methodology consists of four main steps: knowledge def-
inition, linked open data, knowledge extraction and classification, and knowledge rep-
resentation (Fig. 1). We use the word “knowledge” in our system because we enrich the
data with semantic information.
The first step in our system is to backup data in ontological format. This allows us
to represent them formally and semantically. The data used in our study are open data
from the Website “data.gouv.fr”. Once the data are well represented, we take the two
ontologies, in the second stage, and we try to link them with the online data as dbpedia,
and others. All OWL files are saved in a knowledge base.
6 http://data.linkededucation.org/linkedup/catalog
7 http://www.w3.org/community/opened
10
Linked open Knowledge
Geography
Positions
data definition
Dbpedia
...
Link data
Universities employment
Link data of the two ontologies Ontology Ontology
OWL files
Knowledge base
Knowledge Knowledge
extraction & representat-
classification ion
User request
Treatment of
user request
Classification Classified data
process Get user request / present
classified data
Fig. 1. Our global approach
The first three steps are in the systems level only while the fourth is in interaction
with the human being. Indeed, the human being formulates his query in the search en-
gine and sends it to the system. This request is received by the system in the third step
of our methodology (knowledge extraction & classification). It treats the user’s query
and transforms it into SPARQL query in order to apply it on our OWL related files.
The resulting data, from this extraction, will be sorted and sent back to the fourth step
to present them to the user.
Currently the first two steps are performed only once. We do not manage the evolu-
tion of ontology over time. Whereas the last two steps are triggered for each request
sent by user.
4.1 Knowledge definition
As we mentioned before, we use the data available on the government website. These
data can be extracted from this website only in Excel format (Fig. 2). To represent the
data in a more formal and semantic way, we have chosen to transform them into OWL
language. This language allows to describe ontologies, that is to say, it defines termi-
nology to describe specific areas. Ontologies have shown in recent years their ability to
model a range of knowledge in a given field. The transformation of our data in OWL
was done with an open source software RDBToOnto [10].
11
Fig. 2. Excerpt of data from data.gouv.fr website
Before the data is transformed into ontologies we clean them to optimize their quality
whether that of higher education institutions or employment. Our data cleaning process
will affect incomplete, noisy and inconsistent data. As the result of RDBToOnto is un-
satisfactory, the cleaning process is done manually by a human expert.
4.2 Linked open data
This step consists of taking the OWL files from the knowledge base and link them with
open data available online such as the geographical data from INSEE. Indeed, the two
OWL files on which we work contain geographical data. Therefore, we have linked
these data with the geographical data of INSEE as in the following example.
Fig. 3 shows the geographic data (municipality, department, and region) of a univer-
sity and the rate of employment / unemployment by region and how we related them to
the open data on the Web.
établissement public à caractère administratif
Centre national d'enseignement à distance
http://www.freebase.com/m/0d98ld
86960
12
…….
…….
……..
Enseignement scolaire
0861288H
ASTERAMA 2 AV DU TELEPOR
CHASSENEUIL CEDEX
UU86601
http://www.wikidata.org/entity/Q2350714
300
http://www.cned.fr/
214
EPA
Opérateurs LOLF Hors MIRES 2014
Poitiers
86
Vienne
46.6512
0.372263
A13
http://www.cned.fr/rss/communiques-de-presse.xml
0549493400
Fig. 3. Extract from higher education ontology
Then we have linked other data such as areas or trainings. As the employment rate
is also classified by domain (agriculture, commerce, computer, construction ...), and
institutions offer trainings (IT, networks, mechanical, civil Engineering, political sci-
ence, medicine ...) that are applied in specific areas, we can well link these two fields
(Fig. 4). However, these data are not available in open data until now so we created our
own links to connect them. TrainingLevel corresponds to Master, Bachelor, and Engi-
neer degree.
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University Ontology IsLocated
University City
HasTraining IsIn
Training Country
HasTrainingLevel HasDomain
TrainingLevel Domain
HasTrainingLevel HasDomain
Degree Unemployment Ontology
Fig. 4. Linking university and unemployment ontologies
4.3 Knowledge extraction & classification
This step is divided into two distinct parts: the extraction and classification.
In the first part, the system retrieves the request submitted by the user as shown in
(Fig. 6) and reformulates it in SPARQL query language to execute it on OWL files.
The result of this query will then be classified by the most relevant to user (Fig. 7).
Indeed, given a user query, traditional search engines output a list of results which are
ranked according to their relevance to the query. However, the ranking is independent
of document topic. Therefore, the results of different topics are not grouped together
within the result output from a search engine. This can be problematic, as the user must
scroll though many irrelevant results until his desired information need is found. This
might arise when the user is a novice or has superficial knowledge about the domain of
interest, but more typically, it is due to the query being short and ambiguous. Therefore,
to avoid this problem we will present in the future a classification method for results
search to satisfy the request of user.
String sparqlQuery = "PREFIX ontologie1:\n" +
"PREFIX rdf:\n" +
"PREFIX rdfs:\n"+
"SELECT ?nom_univ ?nom_formations \n" +
"WHERE {\n" ;
14
if(!domaine1.equals("Choisissez_un_do-
maine"))
{
sparqlQuery += " ?formations ontologie1:has-
Domaine ontologie1:"+ domaine1 +" .\n" ;
}
else // Si l'utilisateur demande tous les
domaines
{
lblNomdomaine.setText("Tous les do-
maines");
panel_res_3.setBounds(0, 0, 785, 460);
}
…
…
{sparqlQuery += "?formations ontologie1:hasNiveauForma-
tion ontologie1:"+ ite +" .\n";}
…
…
sparqlQuery += "?universites ontologie1:aCommeFormation
?formations .\n";
…
…
sparqlQuery += "{ ?universites ontologie1:new_reg_nom
\""+ region + "\" } UNION ";
…
…
}
Fig. 5. Excerpt of SPARQL query
4.4 Knowledge representation
This step allows us to offer a website to the user where he can submit his request for
a search and receive the corresponding information. The system then retrieves the query
when the user submits it and sends it to the step "Knowledge Extraction & classifica-
tion" to treat it. The system receives by return the query result already classified and
displayed it to the user by order of relevance.
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Fig. 6. GUI of our system
Fig. 7. Results of research
16
In this example, the student search for a training in chemistry, commerce or law
domain in four regions in France.
Fig. 7 shows the result of his search. It is classified by domain. In each one, student
can find the name of training and the university that offers it. The system shows also
the percent of unemployment by domain and by training level depending on the infor-
mation we extracted from the data.gouv.fr website.
We didn’t implement yet the classification system to classify results in each domain
by the most relevant to the student.
5 Conclusion and future work
We presented in this paper an idea for universities management system. The aim of
this system is to assist students in the choice of their domain of specialization. This
choice can be done by selecting different criteria: university ranking, location, scholar-
ships, and the average of unemployment. Our system collect information from govern-
ment open data, clean them and represent them formally in ontological format.
Our work is still in progress. We worked now on the phase of linking our ontologies
to available open data or create our own open data when necessary.
We presented in section 4 our first results. We will improve this system by working
on different points such as creating new vocabularies, implementing the classification
system and allow students to submit their queries in natural language.
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