Ontology engineering of automatic text processing methods

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6620~6628
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6620-6628  6620
Journal homepage: http://ijece.iaescore.com
Ontology engineering of automatic text processing methods
Zhanna Sadirmekova1,2
, Jamalbek Tussupov3
, Aslanbek Murzakhmetov1
, Gulkiz Zhidekulova1
,
Aigul Tungatarova1
, Murat Tulenbayev1
, Shynar Akhmetzhanova1
, Zhanar Altynbekova4
,
Gauhar Borankulova1
1
Department of Information Systems, Faculty of Information Technology, M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan
2
Institute of Information and Computational Technologies, Committee of Science of the Ministry of Education and Science of the
Republic of Kazakhstan, Almaty, Kazakhstan
3
Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University,
Astana, Kazakhstan
4
Department of Computer Science, Kazakh National Women’s Teacher Training University, Almaty, Kazakhstan
Article Info ABSTRACT
Article history:
Received Apr 14, 2023
Revised May 3, 2023
Accepted May 6, 2023
Currently, ontologies are recognized as the most effective means of
formalizing and systematizing knowledge and data in scientific subject area
(SSA). Practice has shown that using ontology design patterns is effective in
developing the ontology of scientific subject areas. This is due to the fact
that scientific subject areas ontology, as a rule, contains a large number of
typical fragments that are well described by patterns of ontology design. In
the paper, we present an approach to ontology engineering of automatic text
processing methods based on ontology design patterns. In order to get an
ontology that would describe automatic text processing sufficiently fully, it
is required to process a large number of scientific publications and
information resources containing information from modeling area. It is
possible to facilitate and speed up the process of updating ontology with
information from such sources by using lexical and syntactic patterns of
ontology design. Our ontology of automatic text processing will become the
conceptual basis of an intelligent information resource on modern methods
of automatic text processing, which will provide systematization of all
information on these methods, its integration into a single information space,
convenient navigation through it, as well as meaningful access to it.
Keywords:
Automatic text processing
methods
Domain ontology
Ontology design
patterns
Content patterns
Ontology completion
Ontology engineering
This is an open access article under the CC BY-SA license.
Corresponding Author:
Zhanna Sadirmekova
Department of Information Systems, Faculty of Information Technology, M.Kh. Dulaty Taraz Regional
University
60 Tole bi str., Taraz, 080000, Kazakhstan
Email: janna_1988@mail.ru
1. INTRODUCTION
Ontologies are extensively used to formalize knowledge in the areas of scientific subjects. With the
aid of ontology, it is possible to assure their uniform and consistent description as well as the convenient
presentation of all the required ideas of the simulated domain. A scientific subject area (SSA) is understood
as a subject area (SA) that covers a specific scientific discipline or knowledge, including its objects and
subjects of research, characteristics and used research methods. Currently numerous strategies and
approaches are suggested to speed up the time-consuming process of developing an ontology for any topic
area [1]–[4]. Accordingly, intensively developing an approach based on the ontology design patterns
application (ODP) [5]–[8]. According to this approach, ODP is documented descriptions of proven solutions
to typical problems of ontology modeling [9]. They are developed to assist and streamline the creation of

Int J Elec & Comp Eng ISSN: 2088-8708 
Ontology engineering of automatic text processing methods (Zhanna Sadirmekova)
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ontologies and aid developers in avoiding common blunders in ontology modeling. Despite the fact that using
ontology design patterns reduces the need for human resources and raises the standard of ontologies being
created, currently only one method for building ontologies i.e. eXtreme design methodology [10], suggested
within the NeOn project [11], openly declares the use of ODP.
Note also that there are not many ontology development tools that support the use of ODP. These
include, a plugin for NeOn project development tools of ontology, as well as a plugin for the web protégé
ontology editor [12], [13]. However, these funds cover only a part of possible problems associated with
patterns. So, there are no instruments supporting the patterns searching, construction and extraction from
ontologies, and very few instruments supporting the patterns collection, discussion and also dissemination.
To some extent, the latter include of ontology design patterns catalogs [14]–[17], which have also actively
developed now.
The paper considers an approach to the implementation of such kind of ontology design patterns as
content patterns [18], which play an important role in the development of ontologies of modern methods of
automatic text processing (ATP) proposed by the authors. The ontology of ATP modern methods includes
both classical ATP methods and methods using machine learning. The papers [19]–[21] existing ontologies
containing ATP methods were analyzed. At the moment, there is an ontology of machine learning [22], [23]
which contains a small set of ATP methods based on machine learning. However, existing ontologies cannot
give an idea of the whole variety of this type of method. In addition, many new methods and models have
recently appeared that have not yet been reflected in previously developed ontologies. To systematize data
and information resources, to organize meaningful access to them, the ontology of the subject area
“automatic text processing” developed within the framework of this paper will be used, and software basis
will be used as a standard and tool of semantic web technologies [24].
2. PROBLEM STATEMENT AND ONTOLOGY MAIN DEFINITIONS
Let be given the SA ontology, the replenishing rules of this ontology, the syntactic and semantic
model of the SA language, the terms dictionary and input data in final text form in a natural language
containing information for replenishing the ontology. We consider that ontology 𝑂 subject area includes the
following elements: i) a finite non-empty set of classes 𝐶𝑂 that describe the subject area concepts; ii) a finite
set of data domains 𝐷𝑂; and iii) a finite set of attributes with names from the set 𝐷𝑎𝑡𝑂∪𝑅𝑒𝑙𝑂, while the data
attributes from 𝐷𝑎𝑡𝑂 accept values from some data domain in 𝐷𝑂, and the values of relationship attributes
from 𝑅𝑒𝑙𝑂 that model relationships between classes are instances of classes from 𝐶𝑂.
Every class 𝑐 ∈ 𝐶𝑜 determined by set attributes: 𝑐 = (𝐷𝑎𝑡𝑐, 𝑅𝑒𝑙𝑐), where each data attribute
𝛼 ∈ 𝐷𝑎𝑡𝑐 ⊆ 𝐷𝑎𝑡𝑜 mapped domain 𝑑𝛼
𝑐
⊆ 𝐷𝑜 with values in 𝑉𝑑𝛼
𝑐 , and every attribute relationship 𝑝 ∈ 𝑅𝑒𝑙𝑐 ⊆
𝑅𝑒𝑙𝑜 accepts values classes 𝑐𝑝 ⊆ 𝐶𝑜. All attributes set in class 𝑐 denoted as 𝐴𝑡𝑟𝑐 = 𝐷𝑎𝑡𝑐 ∪ 𝑅𝑒𝑙𝑐. For
attribute 𝛾 his class is denoted as 𝑐𝛾
and his values set as 𝐷𝛾
. Among class attributes, singled out non-empty
of key attributes set 𝐴𝑡𝑟𝑐
𝐾
, which can be attributes of both data and relationships. Set 𝑎 = (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎) is
an instance of class 𝑐𝑎 = (𝐷𝑎𝑡𝑐𝑎
, 𝑅𝑒𝑙𝑐𝑎
)(𝑎 ∈ 𝑐𝑎), if and only if every attribute data in 𝐷𝑎𝑡𝑎 has name 𝛼𝑎 ∈
𝐷𝑎𝑡𝑐𝑎
with values 𝑉𝛼𝑎
from 𝑉𝑑𝛼𝑎
𝑐𝑎 , and every attribute relationship in 𝑅𝑒𝑙𝑎 has name 𝑝𝑎
∈ 𝑅𝑒𝑙𝑐𝑎
with values
𝑉𝑝𝑎
as instances of classes from 𝑐𝑝. Key attributes data are always unambiguous, i.e. every key attribute in
each instance of ontology maybe have only one value. Key attribute relations correspond to bijective
relations. We consider ontology without synonyms classes and attributes data, i.e. ∀𝛼1, 𝛼2 ∈ 𝐷𝑎𝑡𝑜: 𝑑𝛼1
≠
𝑑𝛼2
and ∀𝑐1, 𝑐2 ∈ 𝐶𝑜: 𝐴𝑡𝑟𝑐1
≠ 𝐴𝑡𝑟𝑐2
. Class 𝑐2 inherits class 𝑐1 if and only if ∀𝑎 ∈ 𝑐2: 𝑎 ∈ 𝑐1.
Informational content 𝐼𝐶𝑜 ontology 𝑂; this a set of copies classes of this ontology. Problem
replenishment ontology is the calculation of informational content by given input data for a given ontology.
There we define a set of 𝐴 information objects (i: objects) retrieved from input data and relevant copies
ontology. Every informational object 𝑎 ∈ 𝐴 has a view (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎, 𝐺𝑎, 𝑃𝑎), where:
a. Class 𝑐𝑎 ∈ 𝐶𝑜
b. 𝐷𝑎𝑡𝑜 is a set of attributes data 𝛼𝑎 = (𝛼, 𝑉𝑎𝑙𝛼𝑎
), where
− Name 𝛼 ∈ 𝐷𝑎𝑡𝑐𝑎
− 𝑉𝑎𝑙𝛼𝑎
is set of attributes data 𝑣
̅ = (𝑣𝑣
̅, 𝑠𝑣
̅), where:
values data 𝑣𝑣
̅ ∈ 𝑑𝛼
𝑐𝑎
and 𝑉𝛼𝑎
= {𝑣𝑣
̅|𝑣
̅ ∈ 𝑉𝑎𝑙𝛼𝑎
} and 𝑠𝑣
̅ is structural information (position in input
data)

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c. 𝑅𝑒𝑙𝑎 is set of attributes relations 𝑝𝑎
= (𝑝, 𝑉𝑝𝑎
), where
− name 𝑝 ∈ 𝑅𝑒𝑙𝑐𝑎
− 𝑉𝑝𝑎
is i - objects set of class 𝑐𝑜
̅ ∈ 𝑐𝑝𝑎
d. 𝐺𝑎 is grammatical information (morphological and syntactic signs);
e. 𝑃𝑎 is structural information (many positions in input data).
Denote a set of all attributes i-object 𝑎 as 𝐴𝑡𝑟𝑎 = 𝐷𝑎𝑡𝑎 ∪ 𝑅𝑒𝑙𝑎. Every i-object natural way
corresponds to some instance ontology: if 𝑎 = (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎, 𝐺𝑎, 𝑃𝑎) is some i-object, that his
corresponding copy ontology is 𝑎′ = (𝑐𝑎, 𝐷𝑎𝑡𝑎′, 𝑅𝑒𝑙𝑎′). Every attribute 𝛼′ ∈ 𝐷𝑎𝑡𝑎′ has values 𝑉𝛼𝑎
. Every 𝑝 ∈
𝑅𝑒𝑙𝑎′ has values 𝑉𝑝𝑎
.
3. DEVELOPMENT OF ONTOLOGIES SUBJECT AREA “AUTOMATIC TEXT PROCESSING”
The ontology of “automatic text processing” subject areas as shown in Figure 1 includes the
systematization of modern ATP methods, a specification of properties, relationships between them,
techniques and areas of their publications, and application. Systematization of all information on the
specified methods can be carried out on the next basics: by purpose (solved applied problem types), and by
areas of use. The core of the ATP ontology is formed by the ATP class, which defines the main properties of
the ATP methods, and its subclasses, which are used to represent the types of solutions to problems using
methods. Such classes are machine translation, abstracting, annotation, sentiment analysis, rubrication,
classification and text pasteurization, and building knowledge bases.
Figure 1. Ontology of the subject area “automatic text processing”
To build an ontology and its initial content, a technique was used to develop ontologies using basic
ontologies that include only the most general entities that do not depend on a specific subject area and ODP
[25], [26] which are documented descriptions of proven solutions to typical problems of ontology modeling
in practice. The use of such patterns not only improves the quality but also greatly facilitates the development
of an ontology since it can involve experts in the modeled area who do not have the skills of ontology
modeling. To assess the quality of the ontology was developed a methodology [27], on the basis of which the
involved experts carried out an experimental assessment of the created ontology, including an assessment of
the degree of agreement of the experts. Metrics for evaluating various ontology properties that do not require
expert work are also considered. As a result of the research, we propose a methodology for the development
of intelligent information resource of automatic text processing (IIR ATP), it offers the architecture and
algorithm for the development of IIR ATP. The principles and approaches underlying the methodology
determine the following main features: i) focus on semi-formalized software; ii) independence from software;
iii) focus on the maximum use of ready-made developments (both copyright and third-party);
iv) use of semantic web technologies and service-oriented approach, information system supporting scientific
and educational activities (ISSEA) development technologies; v) use of the ISSEA shell as a framework for

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the future IIR ATP; and vi) openness and scalability of the proposed tools; convenience and low entry
threshold for the use of the proposed funds. The format for describing ATP methods is supplemented with
elements that serve to describe the context development and use of ODP. For these purposes, the ontology of
ATP methods includes the following classes: scope, activity, task, publication, person, organization, and
information resource. To associate methods with instances of these classes, the ontology of ATP includes
relations that allow link ATP with SA, persons, organizations, and projects in which they are used, as well as
with publications and information resources where they are described. The ontology describes most fully the
ATP methods implemented in the proposed IIR ATP system [28] using the following ODP templates:
structural logical patterns, content patterns, presentation patterns, and lexico-syntactic patterns (LSP)
[29]–[31].
Necessity of use structural logical patterns arose due to the lack of expressive means in the web
ontology language (OWL) [32] for representing complex entities and constructions that are relevant in the
construction of ATP ontologies, in particular, many-place and attributed relations (binary relations with
attributes), as well as ranges of valid values determined by the developer of the ontology. Pattern
specialization can consist of renaming, in specifying the names and values of its properties (attributes and
relations). Figure 2 shows the specialization of patterns on the example of the structural logical pattern
“binary attributed relation”. The central place in this pattern is occupied by the auxiliary class Relation with
attributes, with which the base classes that model the arguments of a binary relation are associated, through
the relationships “is an argument” and “has an argument”. At the same time, in the pattern (in link labels) it is
indicated that there should be one such argument. The attributes of a binary attributed relation are modeled
by the properties of the relation class with the attributes “has an attribute” and “has an attribute from
domain”. In general, such a relationship may have no attributes, as reflected in the link labels that represent
those properties. The concretization (meaning) of the pattern consists in substituting specific property values
into it.
Figure 2. Binary attributed relationship patterns and its specialization
The pattern “area of allowable values” is intended to set the possible values of any property of the
class, when is known in advance the whole values set (usually string) and can be stated at the stage of
develop. Content patterns are designed to uniform provide and consistent of concepts representation used in
ATP and their properties. Content templates are to provide a uniform and consistent representation of ATP
concepts and their properties. Such patterns were developed for concepts that are typical for most SSA:
subject of research, object of study, section of science, task, method, scientific result, project, activity,
organization, person, publication, and information resource. For each of these patterns, a set of proficiency
testing questions is defined. With these questions, the optional and mandatory compositions of pattern
elements ontology are identified and requirements for them are described, which are presented in the
restrictions and axioms forms. For each pattern representing the concept of SA, a set of key attributes has
been compiled that uniquely identify concept specific instance. Figure 3 shows a pattern for representing
“ATP methods” concept. The pattern description elements are represented by the obligatory classes of the
ontology task, science section, organization and person, optional classes activity, and scientific result, and the
relations “solves”, “used in”, “implemented in”, and “has an author”. In pattern representing the concept of
“ATP methods”, there is one key attribute “name”.

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Figure 3. “Method” concept patterns
Competency examples assessment questions representing ATP methods content pattern: “what is
methods name?”, “who is methods author?”, “when was method proposed?”, “what problems are solved
using the method?”, “what activity uses the method?”, “in what scientific results is the method
implemented?”, “who is using the method?”, “what organizations use the method?”.
4. ARCHITECTURE OF AN INTELLIGENT RESOURCE BASED ON MODERN ATP METHODS
IIR ATP consists of the following components as shown in Figure 4; an ontology of ATP methods, a
repository of ATP methods, basic ontologies repository, a dictionary of scientific lexicon, data and ontology
editors, a subsystem to automatic replenishment of an LSP based ontology. The repository of ATP methods is
built on basis of ATP methods ontology and includes realizations of ODP. At the same time, structural-
logical patterns presentation patterns, content patterns are implemented by OWL language means, while LSP
is presented in a description language on specialized template [33].
The automated ontology building system (AOBS) supports the building methods of SSA ontology
based on basic ontologies that contain the most general concepts that are typical for most SSA. For this
reason, the system consists of a repository of basic ontologies such as: scientific knowledge ontology,
scientific activity ontology, the basic ontology of problems and basic ontology of information resources [34].
All base ontologies have characteristics in OWL language. Content patterns have been developed and
included in the ATP repository for the most important basic ontologies concepts. The developed ontology
model was implemented in the Protégé 5.5.0 ontology editor, Figure 5.
Figure 4. Architecture of the automated ontology building system

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Figure 5. Protege editor
The system includes data editor for convenient use of ATP methods, that enables replenishing the
ontology of the SSA by concrete definition of content patterns included in ATP methods repository. The
dictionary of scientific lexicon contains semantically marked terms used in scientific texts to describe the
essence of various ATP methods. It is used to extract subject vocabulary from texts and automatically
generate an SA dictionary, as well as for subsequent automatic text analysis using LSP. The subsystem of
automatic ontology replenishment is intended to enter information extracted from texts in natural language
into SSA ontology. For this, LSP is used, built on content patterns basis and general scientific lexicon
dictionary intelligent information resource is designed to systematize information about modern methods of
automatic text processing and provide meaningful access to it. The work of the resource is organized on ATP
ontology basics, which is its conceptual basis.
The left side of Figure 6 shows the class hierarchy of the ATP ontology. The right side shows a
description of the ATP method, which includes the name of the method, a description of its purpose, a link to
the OWL view, a link to a graphical representation, a set of questions for assessing competence, and links to
projects in which it was developed and used. In addition, IIR ATP is an AOBS user interface that provides
users with access to all repositories and editors that support the development of the SSA ontology, as well as
the subsystem of automatic ontology replenishment based on LSP.
Figure 6. Intelligent information resource

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5. CONCLUSION
This paper describes the ontology model of an intelligent information resource developed by the
authors according to modern methods of automatic text processing. The ontology systematizes information
about the area of knowledge “Automatic text processing” and provides developers of IIR ATP with a single
conceptual basis. The Ontology Design Patterns used in this approach appeared as a result of solving
ontology modeling problems that the authors of the paper encountered in the process of developing
ontologies for various scientific subject areas. The use of ontology design patterns makes it possible to
provide a uniform and consistent representation of all the entities of the scientific subject areas of ontology,
to reduce the number of errors in ontology modeling, to increase the “comprehensibility” of the ontology by
developers, and thus to provide the possibility of collective development of ontologies. Since the use of
Ontology Design Patterns greatly simplifies and facilitates the development of the ontology of the scientific
subject areas, it can involve experts in a particular scientific subject area who do not have the skills of
ontology modeling, which can significantly speed up the development of the ontology. Our further research
is aimed at the full-scale implementation of the subsystem for automatic ontology replenishment based on
lexico-syntactic patterns.
ACKNOWLEDGEMENTS
This research was funded by a grant for Financing scientific and technical projects for 2022-2024,
from the Science Committee of the Ministry of Science and higher education of the Republic of Kazakhstan,
grant number “AP14972834”, (Grant No. AP14972834).
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BIOGRAPHIES OF AUTHORS
Zhanna Sadirmekova holds a Ph.D. in Information Systems from L.N.
Gumilyov Eurasian National University, Astana, Kazakhstan. Successfully defended her thesis
on “Development of technology for integration of information systems to support scientific
and educational activities based on metadata and ontological model of the subject area”. She is
currently pursuing postgraduate studies at the Federal State Autonomous Educational
Institution of Higher Education “Novosibirsk National Research State University” in Physics
and Astronomy. Currently, she is Associate Professor of Information Systems Department at
M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan. Research interests: information
technology in education, artificial intelligence, search, digital library, ontology. She has more
than 50 publications. h-index 3. She can be contacted at email: janna_1988@mail.ru
Jamalbek Tussupov Doctor of Physical and Mathematical Sciences, place of
defense - Kazakh National University, specialty code-01.01.06 “Mathematical logic, algebra
and number theory”. Dissertation topic: “Problems of definability and algorithmic complexity
of relations over algebraic structures”. Has over 100 publications, including: 1 manual; 4
monographs; 30 articles in journals of Scopus, 30 articles in journals of the list of COXON
and VAK of the Russian Federation; 5 certificates on official registration of computer
programs used in teaching and research practice, and h-index 5. He can be contacted at email:
tussupov@mail.ru.
Aslanbek Murzakhmetov received Ph.D. degree in 2022 from al-Farabi Kazakh
National University in specialty Information Systems. Currently, he is Associate Professor of
Information Systems Department at M.Kh. Dulaty Taraz Regional University, Taraz,
Kazakhstan. He has more than 20 scientific papers. He was a junior researcher at Al-Farabi
KazNU and local coordinator of the LMPI project of the Erasmus+ program at Dulaty
University. Research interests: pattern recognition and classification; optimization systems,
big data processing, multi-agent systems, stochastic programming methods, methods of
operations research. He can be contacted at email: aslanmurzakhmet@gmail.com.

 ISSN: 2088-8708
6628
Gulkiz Zhidekulova candidate of technical sciences, currently, she is Associate
Professor of Information Systems Department at M.Kh. Dulaty Taraz Regional University,
Taraz, Kazakhstan. She has more than 110 scientific papers, including 4 papers in Web of
Science and Scopus rating publications, 2 monographs, 7 textbooks and 2 copyright
certificates of intellectual property. H-index–1. She was the executor of the project of search
and initiative research work on the topic “Development of software” Unified information
retrieval system of electronic archive “for the State Archive of Zhambyl region”. She can be
contacted at email: gul2006@mail.ru.
Aigul Tungatarova defended her Ph.D. thesis at the Altynsarin National
Academy of Education, Astana. Currently, she is an Associate Professor of the Department of
Information Systems of M.Kh Dulaty Taraz Regional University, Taraz, Kazakhstan.
Research interests: information technologies in education, information security, information
protection, methods of teaching disciplines of the specialty. She has more than 100
publications, including 12 articles in the journals of the Scopus, 11 articles in the journals of
the Higher Attestation Commission of the Republic of Kazakhstan; H-index-2. She can be
contacted by e-mail: at.tu@mail.ru.
Murat Tulenbayev Doctor of Technical Sciences, Professor of Information
Systems Department at M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan. He has
more than 113 scientific papers. Research interests: digital filtering and information
processing (including wavelet technologies), information and measurement systems, computer
simulation and process management, environmental monitoring information systems,
information technologies in education (including distance education technologies). He can be
contacted at email: mtulenbayevt@mail.ru.
Shynar Akhmetzhanova candidate of technical sciences, currently, she is
Associate Professor of Information Systems Department at M.Kh. Dulaty Taraz Regional
University, Taraz, Kazakhstan. She has more than 100 scientific papers, including 2 papers in
Web of Science and Scopus rating publications, 2 monographs, 2 textbooks and 2 copyright
certificates of intellectual property. She can be contacted at email: shina_70@mail.ru.
Zhanar Altynbekova is a 2nd year doctoral student at the Kazakh National
Pedagogical University for Girls 8D01502- Informatics. Has more than 15 scientific works,
including 2 works published in Web of Science and Scopus rating publications. Research
interests: open education system. Department of Informatics and Applied Mathematics,
Almaty, Kazakhstan. She can be contacted by e-mail at janka1930@mail.ru.
Gauhar Borankulova candidate of technical sciences, Associate professor and
Head of Information Systems Department at M.Kh. Dulaty Taraz Regional University. She
has more than 60 scientific papers, including 7 works in the rating publications Web of
Science and Scopus, h-index–2. Research interests: fiber-optic technologies, microprocessor
systems, information systems. Department of Information Systems, Faculty of Information
Technology. Taraz, Kazakhstan. She can be contacted at email: b.gau@mail.ru.

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