Ontology and Query System Implementation of a Computer Science

Program Using Grüninger and Fox’s Methodology

Oswaldo Jair García Franco

, Erick Barrios González

, Mireya Tovar Vidal

José de Jesús Lavalle Martínez

and Carmen Cerón Garnica

Faculty of Computer Science, Benemerita Universidad Autonoma de Puebla,

14 sur y Av. San Claudio, C.U., Puebla, 72570, Mexico

carmen.ceron@correo.buap.mx

Keywords: Ontology, Grüninger and Fox’s Methodology, OWL.

Abstract: This article shows the Grüninger and Fox’s methodology for the implementation of an ontology using OWL

(The W3C Web Ontology Language). The ontology is created from a computer science program, it also shows

some results obtained by a module created in python to add, delete, and consult information about students,

courses and teachers using Owlready2 and RDFLib (packages for ontology-oriented programming in Python).

The consistency of the ontology has been evaluated using the Protegé´s pellet reasoner and queries that answer

specific competence questions defined in the design phase of the methodology. The results show the

advantages of the ontology to manipulate and consult the information about the degree.

1 INTRODUCTION

The idea of adding semantic and ontological metadata

to the network (semantic web) allows us to have more

organized information, ensuring better searches.

Ontologies define the basic terms and relationships

that make up the vocabulary of a specific area, as well

as the rules for combining terms and relationships to

define extensions to the vocabulary (Neches, 1991),

providing the means to explicitly describe the

conceptualization behind the knowledge represented.

That is why ontologies are intended to address a

problem related to unorganized information. In the

present document the design of an ontology is

presented to organize information about courses,

professors and students in a computer science degree.

For the design of the ontology, the Grüninger and

Fox’s methodology (Gruninger, 1995) helped us to

define competency questions based on a scenario, and

express them formally with its axioms, that is, the

possible applications in which the ontology will be

used. We have also relied on (Jean-Baptiste, 2021) for

https://orcid.org/0000-0002-5467-7389

https://orcid.org/0000-0002-2077-7541

https://orcid.org/0000-0002-9086-7446

https://orcid.org/0000-0001-8652-3889

https://orcid.org/0000-0001-6480-6810

the creation of the ontology in OWL and the modules

in Python. There are other works that have addressed

similar problems. In (Doria, 2017) where the use of

ontologies in the educational field is proposed. In

(Mora Arciniega, 2016) an ontological model is

developed for the representation of academic data and

its publication with semantic technology and (Rosell

León, 2016) where an UH-Ontology was designed for

the management of heterogeneous data in

universities. Our contribution is mainly focused on

the management of information regarding

undergraduate courses, it is hoped to provide an

alternative to existing solutions. (Reyes Peña, 2018)

implements an ontology for a master’s program in

computer science, (Flores, 2017) where an

ontological model is designed to represent the

information about the professional practices or

(Tovar, 2017) the social service of an educational

institution, all using the methodology of (PyQt5,

2021) and SPARQL. We also have (Bravo, 2014) that

performs the representation of the academic and

GarcÃ a Franco, O., Barrios GonzÃ ˛alez, E., Tovar Vidal, M., Lavalle MartÃ nez, J. and CerÃ¸sn Garnica, C.

Ontology and Query System Implementation of a Computer Science Program Using GrÃijninger and Foxâ

Zs Methodology.

DOI: 10.5220/0012024300003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 703-708

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

703

institutional context using ontologies but using

SQWRL as a query language.

1.1 Motivation

The implementation of an ontology in Computer

Science degree will allow the administrative staff to

access efficiently to detailed information about

students, courses and professors which allows to have

a scalable system in the future to store more data

related to the study program.

2 METHODOLOGY

The Grüninger and Fox’s methodology (Gruninger,

1995) was applied to the proposed problem. It is

composed of the following steps: Motivating

scenario, informal competency questions,

terminology, competency questions using formal

terminology and axioms.

2.1 Motivating Scenario

The Computer Science degree is made up of an

educational program described by a study plan

document and a graphic map that shows the

information about the courses. The study plan

document describes the classification of courses in

different levels: The basic level is made up of the

General University Training area (FGU) and two

disciplinary areas that correspond to Computer

Science. The training level is made up of 5

disciplinary areas that correspond to: Disciplinary

integration, basic sciences, computer science and

technology that make up the formation of the

computer science degree. Finally, the optative area is

made up of the disciplinary optative courses and the

complementary optative courses, which includes a

range of 54 to 90 hours and 3 to 6 credits. Although

the study plan document contains a brief description

of the courses, the graphic map details, their name,

their code, what are their schedules, laboratory hours,

workhours, theory hours, areas, semesters to which

that belong, what is their required courses (or if they

are a requirement to take another course) and the

number of credits obtained with each one. Within the

curricular structure, the area of disciplinary

Integration promotes the relationship of theory with

practice and is made up of two subareas: professional

practice and integration courses. It also considers

within the categories of optional courses both the

disciplinary and the complementary. A regular,

leveling or regularization student can take basic

and/or optative courses, and they are limited to a

maximum of two courses of four credits each one.

Both students and teachers have a name, age, email,

telephone. Teachers have a worker number, a date of

the first contract and can teach one or more courses,

finally students have a registration, a date of entry and

take several courses depending on the current period

they are studying.

2.2 Informal Competency Questions

From the proposed scenario, to define the actions the

actions carried out in the degree and the elements that

make it up, the following set of questions are

obtained.

 What is the id, name, email, telephone of each

student and teacher?

 What is the name, credits, area of each course?

 What is the schedule in which each course is

taught?

 How many areas are there in the degree and

how many credits is each course in each area

worth?

In order to answer the behavior of the elements

that make up the degree, the following set of

informal competency questions are proposed:

 Which teachers teach a disciplinary optative

course and what course do they teach?

 Which students take optative courses and what

courses are?

 How many laboratory hours does each course

in the general formation area have?

 What courses in the area of technology must a

student take and how many credits does each

have?

 What courses are formative and in what periods

are they studied?

From the scenario and the competency questions,

the classes (objects as mentioned in (Gruninger,

1995)), sub-classes, attributes are defined in the

ontology design.

2.3 Terminology

2.3.1 Class Identification

The description of the classes found is presented in

the Table 1, considering the competency questions.

ISAIC 2022 - International Symposium on Automation, Information and Computing

704

Table 1: Class description.

Class Definition

person Individual of the human species

that includes in this case teachers

students

teacher Person whose profession is to teach

a science, in this case he teaches

one or more courses of the degree

in compute

science.

student Person who receives teachings from

the teacher, is registered and takes

one o

more courses.

course Course on a specific topic.

Example: Differential calculus.

basic-

course

It is a matter of general

training, to ensure adequate

mastery of the scientific

instrument, of analysis in general

and the fundamentals of

methodologies of science and

research in particular (Rivera,

2016).

formative

-course

Its objective is to give the

student deeper knowledge and

specialization in the different areas

of computer science (Rivera, 2016).

optative-

course

It is a course whose objective is

to deepen the student’s learning in

the areas that have been of greatest

interest to him (Rivera, 2016).

disciplina

ry-course

It is an optional course which

covers the student’s graduation

profile and is related to current,

conceptual and procedural

knowledge of the Bachelor’s

Degree in Computer Science

(Rivera, 2016).

desi

course

It is an optative disciplinary

type of which only one course can

be taken, and includes a range of 54

to 90 hours and 3 to 6 credits

(Rivera, 2016).

comple

entary-

course

It is an optional course whose

objective is to offer the student the

opportunity to deepen some of the

areas of disciplinary knowledge

(Rivera, 2016).

2.3.2 Defining Attributes

Based on the previously found classes and the

scenario approach, Table 2 shows the description of

the attributes or data property.

2.3.3 Relationships Definitions Between

Classes

In this section, we have defined the relationship

between each class to establish a hierarchy in the

ontology. Teachers and students belong to the class of

person and course is divided into two different types:

basic and formative; for the formative courses there

are a subclass “Optional”, this subclass has the

attribute “optative type” as well as for optional and

disciplinary courses. In Table 3 we can see the

relationships between classes or object property of the

ontology. For example, a course is taught by a

teacher, the cardinality is 1 to 1.

Table 2: Attributes description.

Attribute Class Type Range Cardinal

name

erson Strin

Limited 1:1

erson Strin

Limited 1:1

erson Strin

Limited 1:1

telephone

erson Strin

Limited 1:1

id_teache

teacher String 8 1:1

admissio

date

student Date Date 1:1

attended_

semeste

student String Positive

Inte

1:1

student_i

student String 8 1:1

schedule course Strin

Limited 1:1

credits course Integer Positive

Inte

1:1

semester course Integer Positive

Inte

1:1

theory_h

ours

course Integer Positive

Inte

1:1

lab_hours course Integer Positive

Inte

1:1

work_ho

urs

course Integer Positive

Inte

1:1

course course Strin

4 1:1

name_co

urse

course String Limited 1:1

area course Strin

Limited 1:1

optative_

course

String Limited 1:1

is_desit optative_

course

boolea

boolean 1:1

Ontology and Query System Implementation of a Computer Science Program Using GrÃijninger and Foxâ

Zs Methodology

705

Table 3: Object property.

Relation Domai

Range

Cardin

ality

Predicate

taught_b

course teacher 1:1 taught_by(

course,

teache

)

taken_by course student 1: N taken_by(c

ourse,stud

ent)

teaches teacher course 1: N teaches(tea

cher,cours

takes student course 1: N teaches

(student,

course)

requires course course N: N requires(co

urse,cours

2.4 Competency Questions Using

Formal Terminology

The formal competency questions place restrictions

on which axioms will be included.

1. Which teachers teach a disciplinary optative

course and what course do they teach?

∃$x$n$oc$nc

taught_by((optative_course($oc) ∧

name_course($oc,$nc)), (teacher($x) ∧

name($x,$n)))

2. Which students take optative courses and

what courses are?

∃$x$n$c$nc

(taken_by (course($c) ∧

name_course($c,$nc)), (student($x) ∧

name($x,$n)))

3. How many laboratory hours does each course

in the general formation area have?

∃$lh$c$nc$a

(lab_hours($lh) ∧ (course($c) ∧

name_course($c,$nc)) ∧ area ($a))

4. What courses in the area of technology must

a student take and how many credits does

each have?

∃$x$n$c$nc$cr

(taken_by (course($c) ∧

name_course($c,$nc) ∧ credits($c,$cr) ∧

area($c,$a)), (student($x) ∧ name($x,$n)))

5. What courses are formative and in what

periods are they studied?

∃$c$nc$s

(course($c) ∧ name_course($c,$nc) ∧

semester ($c,$s))

2.5 Axioms

Once the elements within the ontology have been

defined, we proceed to define the axioms:

 A student cannot be a teacher at the same time.

¬ [∃$x$y (student ($x) ∧ teacher($y))]

 A student can only take a DESIT course.

¬ [ ∃ $oc$bc (optative_course($oc) ∧

basic_course($bc))]

 A optative course cannot be a basic course.

(takes (student($s), ((course($c) ∧ (is_desit($)

= True)) = 1))))

 Optative courses are limited to 54 to 90 hours

(optative_course($oc) ∧

((theory_hours($oc,$th) + lab_hours ($oc, $lh)

+ work_hours($oc, $wh)) > 54 ∧

(theory_hours($oc,$th) + lab_hours ($oc, $lh)

+ work_hours($oc, $wh)) < 90))

3 RESULTS AND DISCUSSION

The hierarchy of classes to represent the course,

courses, students and teachers within the Computer

Science degree is shown in Figure 1.

ISAIC 2022 - International Symposium on Automation, Information and Computing

706

Figure 1: Hierarchy between classes.

Figure 2: Final ontology and relationships.

3.1 Implementation of the Ontology

Systems

Equations should be placed on a separate line,

numbered and centered. An extra line space should be

added above and below the equation. Classes,

attributes and relationships have been implemented

using (Jean-Baptiste, 2021), functions have been

created to insert and delete information from forms

using a graphical interface designed with Pyqt5

(PyQt5, 2021).

The implementation of the queries uses two

libraries to complement each other, in this case

Owlready (Baptiste, 2019) does not support certain

SparQL elements:

 ASK, DESCRIBE, LOAD, ADD, MOVE,

COPY, CLEAR, DROP, CONSTRUCT

queries.

 INSERT DATA, DELETE DATA, DELETE

WHERE queries (you may use INSERT or

DELETE instead).

 SERVICE (Federated queries)

 VALUES in SELECT queries

 Parentheses in property path expressions, e.g.

‘(a/rdfs:subClassOf)*’

When a query contains any element listed,

RDRlib executes the query avoiding any kind of

issues.

3.2 Answers to Competition Questions

To show that the ontology and the Python module can

solve the competency questions, the ontology has

been filled with example instances containing: 15

course instances or their subclasses (formative and

basic), 2 teacher instances, and 2 student instances.

To answer the questions previously posed, it is

necessary in the application interface to have a

section to carry out the corresponding queries, the

queries are made in SPARQL in the query section and

the result can be seen in the box below.

The consistency of the ontology was tested before

its implementation in the system through Protegé’s

Pellet reasoner (Protégé, 2021) as it is shown in

Figure 3.

Figure 3: Inferred classes by pellet reasoner.

Figures 4 and 5 show answers to questions 1

and 2 to exemplify the operation of the ontology.

“Which teachers teach a disciplinary optative

course and what course do they teach?” and

“Which students take optative courses and what

courses are?”

4 CONCLUSIONS

The Grüninger and Fox’s methodology (Gruninger,

1995) allowed us to establish a structured semantic

relation to define the classes, properties, relationships

and the restrictions necessary for the implementation

of the ontology that was evaluated through a

consistency test using the Protegé’s Pellet module

(Protégé, 2021) and the answers to the competence

questions mentioned above in the methodology

section. The use of two libraries Owlready2 (Jean-

Baptiste, 2021) and RDFLib (RDFLib Team, 2019)

turned out to be of great importance to obtain results

of more complex queries from the ontology, since

individually each one of the already mentioned

libraries has important limitations, also contemplating

that the performance of the application is limited to the

Ontology and Query System Implementation of a Computer Science Program Using GrÃijninger and Foxâ

Zs Methodology

707

performance of the libraries. In a future work it is

intended to use the results obtained in this paper to

include postgraduate courses information, in addition

to extending the system to improve the management

of student enrolment. Finally, Python as the selected

programming language streamlined the

implementation for each of the ontology modules as

mentioned in (Jean-Baptiste, 2021).

Figure 4: Answer to competence question 1.

Figure 5: Answer to competence question 2.

ACKNOWLEDGEMENTS

The authors would like to thank Benemerita

Universidad Autonoma de Puebla, Mexico. The

present work has been funded by the research projects

VIEP-2021 and VIEP-2022 at BUAP.

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