=Paper=
{{Paper
|id=Vol-2433/paper13
|storemode=property
|title=Methodology of designing computer ontology of subject discipline by future teachers-engineers
|pdfUrl=https://ceur-ws.org/Vol-2433/paper13.pdf
|volume=Vol-2433
|authors=Ivan M. Tsidylo,Hryhoriy V. Tereshchuk,Serhiy V. Kozibroda,Svitlana V. Kravets,Tetiana O. Savchyn,Iryna M. Naumuk,Darja A. Kassim
|dblpUrl=https://dblp.org/rec/conf/cte/TsidyloTKKSNK18
}}
==Methodology of designing computer ontology of subject discipline by future teachers-engineers==
https://ceur-ws.org/Vol-2433/paper13.pdf
217
Methodology of designing computer ontology of subject
discipline by future teachers-engineers
Ivan M. Tsidylo1[0000-0002-0202-348X], Hryhorii V. Tereshchuk1[0000-0003-1717-961X],
Serhiy V. Kozibroda1[0000-0003-4218-0671], Svitlana V. Kravets1[0000-0003-4502-0921],
Tetiana O. Savchyn2[0000-0003-3007-8960], Iryna M. Naumuk3[0000-0001-6804-9191] and
Darja A. Kassim4
1 Ternopil Volodymyr Hnatiuk National Pedagogical University,
2, M. Kryvonosa St., Ternopil, 46027, Ukraine
{tsidylo, g.tereschuk}@tnpu.edu.ua,
vaaaav91@ukr.net, svitlana.kravets@gmail.com
2 Ternopil Ivan Puluj National Technical University, 56, Ruska Str., Ternopil, 46001, Ukraine
savchyn.tanya@gmail.com
3 Bogdan Khmelnitsky Melitopol State Pedagogical University, 20, Hetmanska Str., Melitopol,
72300, Ukraine
naumuk.irina@mdpu.org.ua
4 Kryvyi Rih Metallurgical Institute of the National Metallurgical Academy of Ukraine,
5, Stepana Tilhy Str., Kryvyi Rih, 50006, Ukraine
Abstract. The article deals with the problem of the methodology of designing
computer ontology of the subject discipline by the future teachers-engineers in
the field of computer technologies. The scheme of ontology of the subject
discipline is presented in which the set of concepts of the future computer
ontology and the set of relations between them are represented. The main criteria
of the choice of systems of computer ontologies for designing computer ontology
of the subject discipline: software architecture and tools development;
interoperability; intuitive interface are established. The selection of techniques
for designing ontologies using computer ontology systems is carried out. The
algorithm of designing computer ontology of the subject discipline by the future
teachers-engineers in the field of computer technologies is proposed.
Keywords: computer ontology, knowledge representation, subject field,
educational discipline, model, teacher-engineer, designing.
1 Introduction
1.1 Setting of a problem
One of the important trends in the development of modern computer systems is
ontologically managed information systems. The construction of the latter is closely
connected with the development of theoretical foundations and design methodologies
including a formalized approach, fundamental principles and mechanisms, generalized
architecture and structure of the system, a formal model and methodology for designing
___________________
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
218
ontology of the subject field (including ontologies of educational disciplines), formal
model of presentation of knowledge, generalized algorithms of procedures for
knowledge processing, etc. Accordingly, each of the listed components of the overall
design methodology is a complex information and algorithmic structure and is part of
the field of future teachers-engineers in the field of computer technologies (CT).
Comprehensive solution of these tasks of design will provide an opportunity to enhance
the role of ontological (conceptual) knowledge in solving concrete problems in applied
branches in general and in the educational process in particular [4, p. 9].
Investigations on the study and use of computer ontologies by the future teachers-
engineers in the field of CT cover both cognitive knowledge of knowledge bases and
their means of engineering, and the structure of information (a list of its types and
relationships), necessary for obtaining solutions, means of obtaining and preparing this
information, the procedures for setting tasks for the design of computer ontologies,
solving these problems and obtaining results.
However, the process of designing computer ontologies is complex and lengthy and
requires knowledge of many declarative languages, and in order to facilitate it, there is
a need for the use of certain systems created to design computer ontologies that provide
such interfaces that allow them to conceptualize, implement, verify inconsistency and
documentation. In recent years the number of tools for working with computer
ontologies has increased dramatically (more than 50 editing tools). However, most of
these tools are intended to use existing ontologies by the help of formal languages, such
as: Common logic; Сус; Gellish; IDEF5; KIF; Rule Interchange Format (RIF) and
F-Logic; OWL; XBRL [20]. Therefore, in the process of training future teachers-
engineers, it became necessary to use these systems for designing computer ontologies
that could provide interfaces that would allow operations to be carried out in connection
with the formal representation of sets of concepts and relationships between them.
Computer system ontology (CSO) is a definite answer to this need specifically in the
context of designing computer ontology of the discipline subject field by future
engineers-teachers in the field of CT.
1.2 Analysis of recent research and publications
The process of developing and using ontology in general form is considered in the
works of Sergei Nirenburg [17], Natalya Fridman Noy [18], Victor V. Raskin [23].
Problems of ontologies and their use in computer systems were considered by Vladimir
A. Lapshin [9]. The discovery of the meaning of the concept of “ontology”, given to it
in the computer sciences, the works of James F. Allen [19], Richard Fikes [15], Thomas
R. Gruber [6], and others are devoted to it. Some aspects of the use of computer
ontologies, in the context of intellectual technologies, are discussed in the works of
Vasyl V. Lytvyn [13], Oksana M. Markova [14], Volodymyr V. Pasichnyk [11], Serhiy
O. Semerikov [24], Oleh M. Spirin [26], Illia O. Teplytskyi [25], Ivan M. Tsidylo [31],
Yurii V. Yatsyshyn [12] and others. An overview of the instruments of ontology
engineering was done by Olha M. Ovdii and Galyna Yu. Proskudina [20]. Methods for
creating an interface based on ontology in the environment of the WEB portal were
studied by Kostiantyn V. Liashuk [27], Maryna A. Popova [22], Oleksandr Ye.
219
Stryzhak [28]. The modeling of the ontology of the educational subject field as a means
of integrating knowledge was studied by Vira V. Liubchenko [10], Oleksandr Ye.
Stryzhak [28], Ivan M. Tsidylo [30], Olena H. Yevseieva [32] and others. Modeling the
categorical level of the language-ontological picture of the world was studied by
Oleksandr V. Palagin and Mykola H. Petrenko [21]. Ontological representation of
decision-making processes was done by Yurii P. Chaplinskyi [3]. Using the ontology
of the subject area to eliminate ambiguities in the computer translation of technical texts
was applied by Alla V. Morentsova [16] and others. The works of the above-mentioned
authors contributed to the accumulation and systematization of knowledge for
improving the practical training of students on the creation and use of computer
ontology. However, they do not sufficiently revealed the peculiarities of the creation of
the ontology of a certain subject field in the professional training of future teachers-
engineers of the computer field, taking into account the professional-engineering and
professional-pedagogical activities of future specialists.
1.3 Purpose
The purpose of the article is to justify the methodology of designing the ontology of the
subject field of the discipline as a means and result of systematization of knowledge in
the process of preparation and practical work of the future teachers-engineers in the
field of computer technologies.
2 Results of the study
In the process of training teachers-engineers in the field of CT in the higher educational
institutions, a significant place is the study of intelligent systems, in which ontologies
are used for the formal specification of concepts and relationships that are inherent in a
certain field of knowledge. Since the computer cannot understand how a person does,
the state of things in the world, it must be submitted with all the information in a formal
way. Consequently, ontologies serve as a kind of model of the surrounding world, and
their structure is such that it is easily subjected to machining and analysis. Ontologies
provide the system with information about well-described semantics of given words
and indicate the hierarchical structure of the medium and the relationship of the
elements. All of this allows computer programs to draw conclusions from available
information and manipulate those using ontologies.
The term “ontology” first appeared in the work of Thomas R. Gruber [3], who
considered various aspects of the interaction of intellectual systems directly between
themselves and with man. Intelligent systems are called programs that simulate some
aspects of human intellectual activity. Certainly, any program to some extent deals with
this simulation, because this is the value of a computer for a person: the computer
system allows you to free it from performing some rather complex and sophisticated,
but always the same type of activity: the computer system created, for example, for
editing graphics, cannot be used to manage complex production machines.
220
The task of constructing a description of knowledge is very specific. Therefore,
Gruber has identified a specific term for this task – the explicit specification of
conceptualization. A conceptualization is an abstract, simplified view of the world that
we wish to represent for some purpose. Every knowledge base, knowledge-based
system, or knowledge-level agent is committed to some conceptualization, explicitly or
implicitly. The peculiarity of the task of conceptualization lies in the fact that for the
exchange of knowledge between software systems (in the context of the concept of
artificial intelligence), it is necessary to openly specify their conceptualization, that is
to build a description of this knowledge, moreover, sufficiently formal, that it was
“understood” by other systems.
In the process of developing intelligent systems, the most time-consuming are the
stages of conceptualization and formalization, which are considered in work [2] in the
process of designing a structural model of a neuro-fuzzy expert decision-making system
for determining the professional selection of students for the training of IT specialties.
Consequently, the concept of “engineering ontology” can be defined as a
specification (a formal description) of a certain conceptualization (representation of the
subject field of the investigated task as necessary for a particular task). If the
specification of the interpretation is rather unambiguous, then conceptualization is not
all so simple. Thomas R. Gruber believed that conceptualization was carried out in
terms of classes and attributes [7, p. 911]. The medium of the study problem is
presented in the form of concepts that are described by classes, along with their
properties (attributes) and specific objects – instances of classes.
More specifically, the concept of ontology is defined by David Faure, Claire
Nédellec and Céline Rouveirol [5], who assumes that ontology is an explicit
specification of a particular topic.
This approach involves the formal and declarative representation of some of them
covering the dictionary (or list of constants) for reference to the terms of the subject
industry, limiting integrity to terms, logical statements that limit the interpretation of
terms and how they relate with each other.
Thus, ontology defines a general terminology for scholars who need to share
information in a particular subject area. It covers computer-aided interpretations of the
basic concepts of the subject industry and the interrelationship between them.
Thus, ontology defines a general terminology for scholars who need to share
information in a particular subject area. It covers suitable for interpretation by means
of a computer definition of the main concepts of the subject field and the
interconnection between them. With the increasing popularity of usage of computer
ontologies, their study should be included in the curricula of the higher educational
institutions, since they can generate test tasks, create didactic materials from different
disciplines and branches of knowledge, etc.
However, as noted above, the process of designing computer ontologies is complex
and lengthy and requires knowledge of many declarative languages. Therefore, in the
activities of future CT teachers-engineers it is more appropriate to use CSO that are a
computer program or software package that intended for the construction of computer
ontology from a certain subject field and perform operations related to the formal
representation of sets of concepts and relationships between them, in addition,
221
computer ontologies can be exported to a variety of formats, including invoking RDF
(RDF Schema), OWL and XML Schema, etc.
Regarding the choice of a specific CSO, it should be implemented according to some
of the following criteria [8]:
─ software architecture and development of tools containing information about the
necessary platforms for using the tool;
─ functional compatibility, which includes information on tools and interaction with
other languages and tools for the development of ontologies, translation from some
languages ontologies;
─ the intuition of the interface, covering the work with graphic editors, the co-
operation of several users and the need to provide multiple use of ontology libraries.
However, for the construction of computer ontology of the subject field of the
discipline, future teachers-engineers need to reflect the content of the subject field of
the discipline, which is described in the form of a list of modules, implemented in
various forms of occupations in a particular discipline. While in addition to the content,
form and control of their volume, the corresponding competence for each module are
indicated. Based on the analysis of the subjects and objects of the learning process, the
processes of creating and managing the educational material, one can identify the
following problems that arise during the development of the training course:
─ high complexity of the process of finding new teaching materials;
─ the need to assess the conformity of educational resources with the requirements of
the content of the training course;
─ providing educational resources with the full coverage of the modules of the
discipline in general and the course in particular;
─ excessive coverage of the modules of the discipline and implementation of the
choice of the most optimal educational resource for a particular situation;
─ the need to assess the quality of educational resources.
Thus, in the process of developing the content modules of the discipline, it is important
that on the basis of the system analysis of the specifics of the subject field, the following
requirements for the model of presentation of knowledge and data, which was offered
by Anton V. Anikin [1, p. 62].
1. The model should describe the subject discipline, the structure of the subject field,
the hyponymic relationship between the concepts of the subject industry
(hierarchical relations), the relation of the meronymic (part-whole), the connection
of related terms (which may, in particular, reflect antagonistic relationships, active-
passive relationships, cause-effect relationships, position or paradigmatic
relationships).
2. The model should describe the synonymy of the terms of the subject field of the
discipline, as well as their presentation in various languages.
3. The model should describe: competences of different levels, obtained because of
mastering the discipline; the knowledge, skills and abilities they carry out;
hierarchical relations between these elements.
222
4. The model should describe the electronic educational resources, regardless of their
presentation, place of storage, didactic role and allow the creation of a repository
of such resources based on their descriptions. In this case, the description of the
educational material should include the specified parameters, as well as the
language of presentation of information, the educational goal in the form of the
received competencies, determined through knowledge, skills, and complexity of
educational resources.
5. The model should describe the student’s profile: the choice of language, the current
field of knowledge of the studied discipline taking into account the level of his
knowledge of the various structural elements of discipline, the level of mastering
of individual competencies within the framework of the discipline as well as the
learning objectives described on the basis of the target competences of the
discipline.
6. The model should describe the personalized educational collection as a plurality of
learning resources, which is a subset of the discipline and is included in the
repository, selected based on the student profile, as well as the set of relations
between them, which specify the recommended order of their study.
7. The model should ensure the harmonization and integration of the description of
the teaching resources, the subject discipline, the student profile and personalized
e-learning material through the use of general concepts of the subject industry for
the identification and reuse of: competencies (current and target), data through
knowledge (presented in the form of terms – concepts of the subject field), skills
and assumptions; language (representation and perception of information); the
complexity of teaching material and the level of knowledge of these competencies.
8. The model should provide the possibility to search educational material according
to its parameters, the possibility of building a personalized electronic educational
collection based on the profile of the student and the repository of the subject field.
9. The model should support accumulation, distribution (joint use) and reuse of
knowledge about the subject field of educational disciplines in electronic
educational resources.
10. The model should provide modularity and extensibility.
To implement a model of presentation of knowledge and data that meet the
requirements considered, it is expedient to use an ontological model of presentation of
knowledge, which combines the properties and advantages of other models of
presentation of knowledge and data (graph model, tree-based model, relational model,
semantic network, framing, logical model, etc.).
Solving the tasks of the search and integration of educational material in the
personalized educational collection can be realized in the ontological model because of
the development and inclusion of the corresponding semantic rules in computer
ontology.
The formal model of ontology can be represented as:
O = ,
223
where C – the final set of concepts of the subject field, which determines the ontology
of О; R – the final set of relations between them; F is the final set of functions of
interpretation given on the concepts and / or ontology relations of O.
The restrictions imposed on the set C are not infinity and are not empty (C ≠ ∅). The
sets R and F can be empty, which corresponds to certain types of ontology, when it
degenerates into a simple dictionary (R = ∅, F = ∅), taxonomy of concepts (F = ∅), etc.
One of the possible ontological bases for describing computer ontologies in the
context of the use of CSO by future engineer teachers, presented in the work of Iurii A.
Zagorulko and Olesia I. Borovikova [33, p. 197], are:
─ classes united in taxonomy;
─ relationship (type of links between concepts of the subject industry);
─ functions (a special kind of relationship in which the n-th element of the relationship
is determined by the values of n–1 of the preceding elements);
─ axioms (simulate offers that are always true);
─ specimens (entities) that make up specific objects of the real or abstract world.
Iurii A. Zagorulko and Olesia I. Borovikova [33, p. 199] chose OWL-DL, the language
for the description of ontology, recommended by the consortium W3C, which is widely
used in Semantic Web, is able to be converted by the overwhelming majority of CSO
and allows to use:
─ the logic of the first order for assigning axioms to ontology concepts through the
design of constructs of descriptive logic;
─ existing ontology output machines on OWL-DL, allowing for arguments based on
the rules of descriptive logic;
─ existing free tools for designing ontologies in the OWL-DL language.
OWL-DL combines OWL expressiveness and completeness of computations (all
logical conclusions performed on an ontology basis will be thoroughly calculated) and
extensibility (all calculations are completed at a certain time). The OWL-DL contains
all OWL language constructs that are subject to certain restrictions (for example, a class
may be a subclass of many classes, but cannot be a representative of another class).
Accordingly, the ontological model of the subject discipline of the discipline ODD
(Fig. 1) will be defined as:
ODD = ,
where CDD is the final set of concepts for the ontology of the core curriculum knowledge
(CDD = {cDD1, cDD2, cDD3, cDD4, cDD5, cDD6, cDD7, cDD8, cDD9, cDD10, cDD11, cDD12}, cDD1 is
the DataDomain class for the definition of the subject discipline; cDD2 is the
Competence class for identifying competences in a learning discipline; cDD3 is a
Concept class for defining the concepts (terms) of a discipline subject field that is a
subclass of cDD2; cDD4 is a UCompetence class for identifying universal competencies;
cDD5 is a class of PCompetence for defining professional competencies; cDD6 is a
ZNKCompetence class for general knowledge competencies; cDD7 is a ICompetence
class tool for determining competence; cDD8 is a SOKCompetence class for the
224
definition of social / personal / general cultural competencies; cDD9 is the Skill class for
determining the skills obtained in the subject discipline, which is a subclass of cDD2;
cDD10 is the Ability class for determining the skills obtained in the subject field of the
discipline, which is a subclass of cDD2; cDD11 is a Language class that defines the
language of presentation of information in the discipline subject field; cDD12 is a
Complexity class to determine the level of development of competencies of the
discipline);
InstDD is the set of competencies, concepts of the subject discipline, as well as the
skills represented in the natural language of instances of classes CDD;
InstDD = {iDD1, iDD2, ... iDDm, ... iDDn};
RDD is the final set of relations of the ontology of the knowledge base of the
discipline; (RDD = {rDD1, rDD2, rDD3, rDD4, rDD5, rDD6, rDD7, rDD8, rDD9}; rDD1 is a
hasLanguage ratio, rDD2 is a hasComplexity ratio, rDD3 is a ratio includes, rDD4 is a
hasHierarchicalRelation ratio, rDD5 is a dependOn ratio, rDD6 is a ratio isSynonym, rDD7
is a ratio “is”, rDD8 is a hasTitle, rDD9 is a hasCompetence);
IDD is the set of interpretation rules, IDD =∅.
Fig. 1. Scheme of ontology of the subject field of discipline
The set of concepts for the CDD ontology of the knowledge base of the discipline is
presented in Table 1, and the set of RDD relationships is in Table 2. The defining areas
and the domains of relationship values can be both defined concepts and their daughter
225
concepts within the framework of the ontology. Based on the plurality of these concepts
and the relationship between them using the CSO, future teachers-engineers will be
able to conduct ontological design of the subject field of the discipline they need.
Table 1. The set of concepts of ontology of the subject discipline
Ontology concept Parental concept Concept description
DataDomain Thing Subject field of discipline
Competence Thing Competences
Concept Competence Concepts (terms) of the subject discipline
UCompetence Competence Universal competences of the subject discipline
Professional competence of the subject field of the
PCompetence Competence
discipline
General scientific competence of the subject field of the
ZNKCompetence UCompetence
discipline
ICompetence UCompetence Instrumental competences of the subject discipline
Socio-personal / general cultural competences of the
SOKCompetence UCompetence
subject discipline
Skill Competencе Skills in the subject field of the discipline
Ability Competence Ability of the subject field of the discipline
Language Thing Language of presentation of information
Level of mastery of the competence of the subject
Complexity Thing
discipline
Table 2. The set of relations of the ontology of the subject discipline
Definition Value
Correlation Description
area range
The ratio that sets the language of the
hasLanguage Competence Language
presentation of the ontology
The ratio that sets the level of competence
hasComplexity Competence Complexity
development
The relation of inclusion of competences in
the competence of a higher level, concepts,
includes Competence Competence
skills and abilities – in competence
(through the mechanism of imitation)
Relationship between the two
dependsOn Competence Competence
competencies, concepts, skills or abilities
The relation of synonymy to the concepts
isSynonym Competence Competence
of the subject field and competencies
The relationship “is” between the concepts
is Concept Concept
of the subject field
The ratio of the hierarchy between the
hasHierarchicalRelation Concept Concept
concepts
The ratio that sets out the description of
Competence
hasTitle String competence, concept, skills, ability in form
DataDomain
of text
The ratio that sets the relationship of
hasCompetence DataDomain Competence
competence with the subject field
226
However, the question about the methodology of designing computer ontology
remains unsolved. Now there are several methods of constructing ontologies and they
all are based on the principles proposed by Thomas R. Gruber [7]:
─ Clarity. Ontology must effectively convey the meaning of the terms. Definitions
should be objective, although the motives for introducing terms may be determined
by the situation or the requirements of computing efficiency. To objectivize
definitions, a clearly defined formalism must be used, in which logical definitions
should be defined as logical axioms.
─ Coherence. The ontology must be compatible, that is, the conclusions that can be
drawn from the definitions of concepts and relationships between them must be
compatible with the initial terms. Compatibility should also be maintained for the
concepts informally described. If the conclusions drawn from the formal meanings
are incompatible with the informal descriptions, then the ontology is considered
incompatible.
─ Extendibility. The ontology must be constructed so that it can be used without
additional effort in separate ontology libraries. One of the most important conditions
for such a design is the ability to identify new concepts based on the elements
existing in the ontology so that this does not require the change of the latter.
─ Minimal encoding bias. The projected conceptual scheme should not depend on the
specific language used to record the formal description. Dependence on coding
occurs when the choice of an ontological representation is based on compatibility
with the peculiarity of the language in which the ontology is written. This
dependence must be minimized so that various ontology databases using other
languages can easily understand the projected ontology.
─ Minimal ontological commitment. The ontology must contain at least the facts about
the ontology of the world, which is modeled, while giving the freedom to use this
ontology in others. If the conceptual scheme of the problem is that the description of
the ontology of the world is essential, then this description should, if possible, be
minimal. One should restrict itself to merely recounting the terms of the concepts
without determining the relation between them that is to build a “weak” theory. Then
different bases of ontologies, which determine the ontologies of the world in their
own way, can give meaning to this concept.
However, in the context of designing computer ontology of the subject field of
discipline by means of Protege, it is most appropriate to use the technique of
constructing an ontology proposed by Vasyl V. Lytvyn, Volodymyr V. Pasichnyk and
Yurii V. Yatsyshyn, which includes seven steps [12, p. 319].
Step 1. Define the industry and the scale of the ontology. Work on the development
of ontology should begin with determining its scope. To this end, competence issues
are being developed to verify the relevance of the ontology of a given subject field,
which will continue to serve as a litmus test, giving an idea of the completeness of the
information provided and the level of its detail.
Step 2. The ability to use existing ontologies. It is worth bearing in mind that
somebody worked on the task of creating an ontology, for example, in the field of
material science. Then you need to check the possibility of adapting the existing
227
ontological systems for our specific subject area. Otherwise, work must start from
scratch. Today, many developed ontologies in various subject areas are available and
can be successfully imported into the design environment chosen by the developer.
Step 3. List of important terms in ontology. It is useful to compile a list of all the
terms and their properties, which provide the basic information about the given subject
area. At the beginning, it’s important to get a complete list of terms without worrying
about whether the concept is a class or property.
Step 4. Define classes and their hierarchy. There are several approaches to
constructing a hierarchy of classes: top-down, bottom-up, and combined process.
Step 5. Define the properties of the classes. After determining a certain number of
classes, it is necessary to describe the internal structure of concepts. In step 3, the
classes in the list of terms created were selected. Most of the remaining terms are likely
to be the properties of these classes. All subclasses of the class inherit the property of
this class.
Step 6. Determination of facets properties. Properties may have different facets that
describe the type and factor (power) of the property value, range, and other
characteristics that it may have.
Step 7. Creating instances. The last step is to create separate instances of classes in
the hierarchy. To determine an individual instance you need:
─ choose a class;
─ create a separate instance of this class;
─ enter slot values.
Therefore, for the design of computer ontology of the subject field of educational
discipline for future engineers-teachers in the field of computer technologies, it is
expedient to carry out the following algorithm:
─ Select on the basis of the scheme proposed in Fig. 1, competencies of the first level –
universal (general, instrumental, social-personal competencies of subject discipline)
and professional – on the basis of analysis of the work program of discipline and
matrix of competencies. Describe them as instances of the corresponding classes of
computer ontology of the study discipline (UCompetence, PCompetence,
ZNKCompetence, ICompetence, SOKCompetence).
─ Sequentially allocate competences of the second level by analyzing the list of
acquired knowledge, skills and abilities. Describe them as instances of the
corresponding classes of computer ontology of the discipline (Concept, Skill,
Ability).
─ Based on the analysis of the work program of the discipline and the matrix of
competencies, allocate the third level competencies that are implemented within
each module of the curriculum and describe them as instances of the corresponding
classrooms of the computer ontology (Concept, Skill, Ability).
─ Based on the knowledge of the future teacher-engineer in the field of CT on the
subject discipline and the availability of educational-methodical literature, identify
the competences of lower levels and describe them as instances of the corresponding
classes of computer ontology of the discipline (Concept, Skill, Ability). The
228
recommended number of levels of competence in describing the set of knowledge
discipline is 3 or 4. Additional levels can be used in the description of knowledge in
the form of concepts of the subject area in the case of availability in the individual
modules of discipline a large number of terms of the subject field, which are related
hierarchically. For the description of skills and abilities, in most cases it is up to 3-4
levels of competencies.
─ On the basis of the curriculum work program, as well as knowledge of the subject
area and the analysis of educational methodical literature, identify the relationship
between the competencies described and set them with the following relationships
of the ontology of the discipline: includes (the ratio of the inclusion of competencies
in a higher level of competence), dependsOn (dependency ratio between two
competencies, concepts, skills or abilities). If there is synonymy, set the appropriate
relation to isSynonym. In describing the discipline subject field, use the hasTitle and
hasLanguage relationship to describe the description of the respective competences
in the natural language and language of the description.
3 Conclusions and perspectives of further research
1. The scheme of the ontology of the subject discipline is presented based on which the
future teachers-engineers in the field of CT are. In it, the set of concepts of the future
computer ontology of the subject discipline is represented; and the set of relations
between them, and corresponding definition areas and range of values can be as these
concepts, as well as their daughter concepts in the framework of ontology. Based on
the set of these concepts and the relationships between them using the CSO, future
teachers-engineers will be able to conduct ontological design of the subject field of
the discipline they need.
2. The main criteria for choosing a CSO are: 1) software architecture and tools
development contain information on the required platforms for using the tool;
2) functional compatibility contains information on tools and interaction with other
languages and tools for the development of ontologies, translation from some
languages ontologies; 3) intuitive interface – covers work with graphic editors,
collaborative work of several users and the need to provide multiple uses of ontology
libraries.
3. In the process of selecting a method for designing computer ontologies by means of
CSO, the optimal option in the educational process of the future teacher-engineer is
the method proposed by Vasyl V. Lytvyn, Volodymyr V. Pasichnyk and Yurii V.
Yatsyshyn [12], which provides a number of stages of designing a computer
ontologies.
4. The methodology of designing computer ontology of the subject discipline for the
future teachers-engineers in the field of CT is offere, which includes the scheme of
ontology of the subject discipline, the choice of CSO with the help of which the
project is being implemented. The methodology of designing computer ontology and
the algorithm for computer ontology designing of the subject discipline for future
teachers-engineers in the field of CT is proposed.
229
5. The continuation of scientific research on the given problem is useful in the study of
the dependence of constructed hierarchy concepts in the computer ontology of the
subject discipline and the development of ontologically managed information
systems on their basis.
References
1. Anikin, A.V.: Metod poiska i integratcii raznorodnykh raspredelennykh obrazovatelnykh
resursov na osnove logicheskogo vyvoda na ontologii (A method for the search and
integration of heterogeneous distributed educational resources based on the logical
conclusion on the ontology). Dissertation, Volgograd State Technical University (2014)
2. Buyak, B.B., Tsidylo, I.M., Repskyi, V.I., Lyalyuk, V.P.: Stages of Conceptualization and
Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of
Professional Selection of Pupils. In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 1st
International Workshop on Augmented Reality in Education (AREdu 2018), Kryvyi Rih,
Ukraine, October 2, 2018. CEUR Workshop Proceedings 2257, 112–121. http://ceur-
ws.org/Vol-2257/paper13.pdf (2018). Accessed 30 Nov 2018
3. Chaplinskyi, Yu.P.: Ontolohichne predstavlennia protsesiv pryiniattia rishen (Ontological
description of decision making processes). Problems of Informatization and Management
2(26), 146–150 (2009). doi:10.18372/2073-4751.2.6999
4. Dovhyi, S.O., Velychko, V.Yu., Hloba, L.S., Stryzhak, O.Ye., Andrushchenko, T.I.:
Kompiuterni ontolohii ta yikh vykorystannia u navchalnomu protsesi. Teoriia i praktyka
(Computer ontologies and their use in the educational process. Theory and practice).
Instytut obdarovanoi dytyny, Kyiv (2013)
5. Faure, D., Nédellec, C., Rouveirol C.: Acquisition of Semantic Knowledge using Machine
learning methods: The System “ASIUM”. Technical report number ICS-TR-88-16.
https://pdfs.semanticscholar.org/19c8/fb4d1c782de0ee248b2a4a9b344392bf3995.pdf
(1998). Accessed 21 Mar 2019
6. Gruber, T.R.: The role of common ontology in achieving sharable, reusable knowledge
bases. In: Allen, J., Fikes, R., Sandewall, E. (eds.) Principles of Knowledge Representation
and Reasoning, Proceedings of the Second International Conference (KR91), vol. 2, pp.
601–602. Morgan Kaufmann Publishers, San Mateo (1991)
7. Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge
Sharing? International Journal Human-Computer Studies 43(5–6), 907–928 (1995).
doi:10.1006/ijhc.1995.1081
8. Kozibroda, S.V.: Prohramni zasoby rozrobky ontolohii u protsesi pidhotovky inzheneriv-
pedahohiv kompsiuternoho profiliu (Software tools of ontology development in training
process of future engineers-teachers). Zbirnyk naukovyh prats “Pedahohichni nauky” 74(3),
175–180 (2016)
9. Lapshin, V.A.: Ontologiia v kompiuternykh sistemakh (Ontology in computer systems).
Nauchnyi mir, Moscow (2010)
10. Liubchenko, V.V.: Modeli znanij dlia predmetnyh oblastei uchebnyh kursov (Knowledge
models for subject areas of training courses). Iskusstvennyi intellekt 4, 458–462 (2008)
11. Lozynska, O.V., Davydov, M.V., Pasichnyk, V.V.: Mashynnyi pereklad na osnovi pravyl
dlia perekladu na ukrainsku zhestovu movu (Machine translation based on the rules for
translation in the Ukrainian sign language). Informatsiini tekhnolohii ta kompyuterna
inzheneriia 29(1), 11–17 (2014)
230
12. Lytvyn, V.M., Pasichnyk, V.V., Yatsyshyn, Yu.V.: Intelektualni systemy (Intellectual
systems). Novyi Svit – 2000, Lviv (2013)
13. Lytvyn, V.V.: Metody ta zasoby inzhenerii danykh ta znan (Methods and tools for data and
knowledge engineering). Mahnoliia-2006, Lviv (2012)
14. Markova, O., Semerikov, S., Popel, M.: CoCalc as a Learning Tool for Neural Network
Simulation in the Special Course “Foundations of Mathematic Informatics”. In: Ermolayev,
V., Suárez-Figueroa, M.C., Yakovyna, V., Kharchenko, V., Kobets, V., Kravtsov, H.,
Peschanenko, V., Prytula, Ya., Nikitchenko, M., Spivakovsky A. (eds.) Proceedings of the
14th International Conference on ICT in Education, Research and Industrial Applications.
Integration, Harmonization and Knowledge Transfer (ICTERI, 2018), Kyiv, Ukraine, 14-
17 May 2018, vol. II: Workshops. CEUR Workshop Proceedings 2104, 338–403.
http://ceur-ws.org/Vol-2104/paper_204.pdf (2018). Accessed 30 Nov 2018
15. McGuinness, D.L, Fikes, R., Hendler, J.A., Stein, L.A.: DAML+OIL: An Ontology
Language for the Semantic Web. IEEE Intelligent Systems 17(5), 72–80 (2002).
doi:10.1109/MIS.2002.1039835
16. Morentsova, A.V.: Vykorystannia ontolohii predmetnoi oblasti dlia usunennia
neodnoznachnostei pry kompiuternomu perekladi tekhnichnykh tekstiv (Using the ontology
of the subject area to eliminate ambiguities in the computer translation of technical texts).
In: Aktualni pytania suchasnoi nauky, ІІІ Mizhnarodna naukovo-praktychna internet-
konferentsia: tezy dopovidei, Dnipro, 30 sichnia 2018 r., pp. 82–87.
http://www.kamts1.kpi.ua/sites/default/files/files/morentsova_vykorystannya.pdf (2018)
17. Nirenburg, S., Raskin, V.: Ontological Semantics. MIT Press, Cambridge (2004)
18. Noy, N.F., McGuinness, D.L.: Ontology Development 101: A Guide to Creating Your First
Ontology.
https://protege.stanford.edu/publications/ontology_development/ontology101.pdf (2001).
Accessed 24 Aug 2018
19. Orfan, J., Allen, J.: Learning New Relations from Concept Ontologies Derived from
Definitions. In: Logical Formalizations of Commonsense Reasoning: Papers from the 2015
AAAI Spring Symposium, Stanford University, March 23–25, 2015, pp. 126–129.
https://www.aaai.org/ocs/index.php/SSS/SSS15/paper/viewFile/10322/10084 (2015)
20. Ovdei, O.M., Proskudina, G.Iu.: Obzor instrumentov inzhenerii ontologii (Ontology
engineering tool overview). Elektronnye biblioteki 7(4).
https://web.archive.org/web/20050424145056/http://www.elbib.ru/index.phtml?page=elbi
b/rus/journal/2004/part4/op (2004). Accessed 21 Mar 2018
21. Palahin, O.V., Petrenko, M.H.: Model katehorialnoho rivnia movno-ontolohichnoi kartyny
svitu (The model of the world language ontological сategorial level). Matematychni
mashyny i systemy 3, 91–104 (2006)
22. Popova, M.A., Stryzhak, O.Ye.: Ontolohichnyi interfeis iak zasib predstavlennia
informatsiinykh resursiv v HIS-seredovyshchi (Ontological interface as a means of
representing information resources in the geoinformation environment). Uchenye zapiski
Tavricheskogo natcionalnogo universiteta imeni V.I. Vernadskogo, Seriia: Geografiia
26(65)(1), 127–135 (2013)
23. Raskin, V., Taylor, J.M., Hempelmann, C.: Meaning- and ontology-based technologies for
high-precision language an information-processing computational systems. Advanced
Engineering Informatics 27(1), 4–12 (2013). doi:10.1016/j.aei.2012.12.002
24. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Kiv, A.E.: Computer Simulation of
Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. In: Kiv, A.E.,
Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality
in Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop
231
Proceedings 2257, 122–147. http://ceur-ws.org/Vol-2257/paper14.pdf (2018). Accessed 30
Nov 2018
25. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Markova, O.M., Soloviev, V.N., Kiv,
A.E.: Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson,
Welcome Back. In: Ermolayev, V., Mallet, F., Yakovyna, V., Kharchenko, V., Kobets, V.,
Korniłowicz, A., Kravtsov, H., Nikitchenko, M., Semerikov, S., Spivakovsky, A. (eds.)
Proceedings of the 15th International Conference on ICT in Education, Research and
Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI,
2019), Kherson, Ukraine, June 12-15 2019, vol. II: Workshops. CEUR Workshop
Proceedings 2393, 833–848. http://ceur-ws.org/Vol-2393/paper_348.pdf (2019). Accessed
30 Jun 2019
26. Spirin, O.M.: Pochatky shtuchnoho intelektu (The beginnings of artificial intelligence).
Vyd. ZhDU im. I. Franka, Zhytomyr (2004)
27. Stryzhak, O.Ye., Popova, M.A., Liashuk, K.V.: Metodyka stvorennia ontolohichnoho
interfeisu u seredovyshchi WEB-portalu (Creation method of ontologicall interface in web-
portal). Radioelektronni i kompiuterni systemy 2, 78–84 (2014)
28. Stryzhak, O.Ye.: Ontolohichnyi pidruchnyk – paradyhma formuvannia interaktyvnoi
systemy znan u navchalnomu protsesi (Ontological textbook – a paradigm for the formation
of an interactive knowledge system in the educational process). Kompiuter u shkoli ta simi
7, 7–16 (2016)
29. Tsidylo, I.M., Kozibroda, S.V.: Systems of computer ontologies as a means of forming the
designing competencies of future engineers-teachers. Information Technologies and
Learning Tools 63(1), 251–265 (2018). doi:10.33407/itlt.v63i1.1838
30. Tsidylo, I.: Semantic ontology model of the content module of the course “Intelligent
technologies of decision-making management”. Problemy Profesjologii 1, 131–139 (2014)
31. Tsidylo, I.M.: Pidhotovka inzhenera-pedahoha do zastosuvannia intelektualnykh
tekhnolohii u profesiinii diialnosti (Training of the engineer-teacher to the application of
intellectual technologies in professional activity). Vektor, Ternopil (2014)
32. Yevseieva, O.H.: Modeliuvannia navchalnoi predmetnoi oblasti (Modeling of Teaching
Subject Domain). Shtuchnyi intelekt 1, 79–86 (2009)
33. Zagorulko, Iu.A., Borovikova, O.I.: Tekhnologiia postroeniia ontologii dlia portalov znanii
po gumanitarnym naukam. In: Trudy Vserossiiskoi konferentcii s mezhdunarodnym
uchastiem “Znaniia-Ontologii-Teorii” (ZONT-07), Novosibirsk, 2007, vol. 1, pp. 191–200
(2007)