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{{Paper
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|storemode=property
|title=Methods of Identifying the Correlation of Ukrainian Scientific Paradigms Based on the Study of Defended Dissertations
|pdfUrl=https://ceur-ws.org/Vol-3646/Paper_7.pdf
|volume=Vol-3646
|authors=Hryhorii Hnatiienko,Georgii Gaina,Oleh Ilarionov,Vitaliy Snytyuk,Nataliia Tmienova
|dblpUrl=https://dblp.org/rec/conf/iti2/HnatiienkoGIST23
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==Methods of Identifying the Correlation of Ukrainian Scientific Paradigms Based on the Study of Defended Dissertations==
https://ceur-ws.org/Vol-3646/Paper_7.pdf
Methods of Identifying the Correlation of Ukrainian Scientific
Paradigms Based on the Study of Defended Dissertations
Hryhorii Hnatiienko, Georgii Gaina, Oleh Ilarionov, Vitaliy Snytyuk and Nataliia Tmienova
Taras Shevchenko National University of Kyiv, Volodymyrs'ka str. 64/13, Kyiv, 01601, Ukraine
Abstract
The paper examines the concept of scientific space and the peculiarities of its formation in
Ukraine. The structure of information on defended dissertations is studied. The author
introduces heuristics that, firstly, contribute to structuring the available poorly structured
information, secondly, supplement the information that is not enough to formalise the
problem with known mathematical models, and thirdly, declare the authors' position a priori,
outline the outline of further presentation of the material and the limitations on research that
the authors limit themselves to. The article presents approaches to the analysis of
dissertations defended in Ukraine in 1993-2020. The parameters of defended dissertations,
the values of which are available in open access databases, are listed and assigned identifiers.
An analysis of Ukrainian cities in which a large number of dissertations were completed and
defended is carried out. Such cities are defined as scientific centres. A matrix of statistics on
the relationship between scientific organisations in Ukrainian cities is constructed. An
illustration of the relationships between Ukrainian cities is provided and the scheme of these
relationships is displayed on the map of Ukraine. Thus, the preliminary structuring of the
scientific space in terms of the preparation and defence of dissertations was carried out.
Models are developed and directions for further research of the scientific space of Ukraine
are identified. It is proposed to identify keywords in the abstracts of dissertations based on
text analysis and to identify areas of research by analysing the degree of similarity between
sets of keywords related to dissertations.
Keywords 1
Scientific space, weakly structured system, heuristics, dissertation, adjacency matrix,
research centres, research topics, formalisation
1. Introduction
Scientific activity has a long history and has always been one of the fundamental axes of social
development [1, 2]. From the first attempts to understand nature and society to today's huge scientific
projects, science has constantly tried to expand the horizons of knowledge and understanding.
Today, science is characterised by rapid development and constant change. Society follows the
new discoveries, ideas and technological innovations that are born from scientific research. This
constant development of science, however, is inextricably linked to the concept of scientific
paradigms [3, 4].
Scientific paradigms are the fundamental concepts, theories, methods and approaches that define
the way we understand and explore the world around us. They influence what questions are
investigated, what experiments are performed, and how information is interpreted. Changes in
scientific paradigms can overturn the existing picture of the world, opening up new opportunities for
research and development [5, 6].
The shift in scientific paradigms is not just an evolution of science, but a true revolution that
transforms the way we understand the world. This revolution affects every scientist, researcher, engineer
Information Technology and Implementation (IT&I-2023), November 20-21, 2023, Kyiv, Ukraine
EMAIL: g.gna5@ukr.net (H. Hnatienko); ggaina@gmail.com (G. Gaina); oilarionov@gmail.com (O.Ilarionov); snytyuk@gmail.com (V.
Snytyuk); tmyenovox@gmail.com (N. Tmienova)
ORCID: 0000-0002-0465-5018 (H. Hnatiienko); 0000-0002-7435-3533 (O. Ilarionov); 0000-0002-9954-8767 (V. Snytyuk); 000-0003-
1088-9547 (N. Tmienova)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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and consumer of scientific information. Understanding this impact is an important task for the scientific
community and society as a whole. In this article, we will look at this task in more detail and detail.
2. The urgency of the problem
The study of the impact of changing scientific paradigms on the scientific space is extremely
relevant in the modern world, as the scientific community and society as a whole face a number of
significant challenges and questions that require answers.
1. Scientific Progress and Innovation: Modern science is the engine of technological and
innovative progress. Changes in scientific paradigms can open up new opportunities for the
development and improvement of technologies that affect all areas of life.
2. Globalisation of Science: Science is becoming increasingly global, with scientists from
different countries collaborating and sharing knowledge. Understanding the impact of scientific
paradigms on the global scientific space can improve cooperation and mutual understanding.
3. Interdisciplinarity: The boundaries between different branches of science are becoming less and
less sharp. Shifting scientific paradigms can foster an interdisciplinary approach that allows complex
problems to be addressed from multiple perspectives.
4. Public Role of Science: Society is increasingly looking to science to address important issues,
from climate change to health to economic issues. Understanding the impact of scientific paradigms
on such societal challenges helps to direct scientific potential in the right direction.
5. The Future of Education: Shifting scientific paradigms are affecting how young people learn
and what skills and knowledge are becoming important. Understanding this impact is important for
the development of education systems [7].
6. The role of Ukrainian science: Ukraine, like other countries, faces the challenges of the
globalised scientific space. Understanding the impact of changing scientific paradigms on Ukrainian
science and the scientific space is important for the country's development in the context of the global
scientific community [8, 9].
This article aims to explore the impact of scientific paradigms on the scientific space from
different perspectives and help us better understand how these changes affect our scientific
community and society as a whole. By analysing this impact, we can better adapt our actions and
decisions to the requirements of the modern world and contribute to further scientific development.
3. Purpose and objectives of the study
The purpose of this article is to study the impact of scientific paradigms on the modern scientific
space and to reveal its main characteristics. Formalising these characteristics will help to improve the
structure of the scientific space and understand the interaction between scientific paradigms and the
scientific community.
Objectives: The following objectives have been identified to achieve the above goal:
● Identification of Research Centres: The study aims to identify the main centres, cities that play
a key role in research and thesis defence.
● Analysis of the Frequency of Dissertation Completion and Defence: This task involves
determining how often dissertations are completed and defended in different cities and organisations.
● Determination of Links and Networks between Cities: The study should include an analysis of
the links and interactions between cities as defined by scientific cooperation.
● Visualisation of the Geography of Scientific Research: To better understand the geography of
the research space, the study will include visualisation of the location of cities and organisations
where theses were conducted.
● Research on the Impact of Changing Scientific Paradigms: The study examines the relationship
between the change of scientific paradigms and the development of the scientific space, as well as the
impact of these changes on the structure and functioning of the scientific community.
● Analysis of Interdisciplinary Relations: The study assesses the interrelationships between
different fields of science and the impact of scientific paradigms on the development of
interdisciplinary research.
65
4. Status of the problem development
The current stage of development of human civilisation, defined as the transition to a knowledge
society, is characterised by qualitatively new requirements for the development of science. During the
years of independence, more than 130 thousand PhD and more than 20 thousand doctoral dissertations
have been defended in Ukraine [10, 11]. It can be confidently stated that dissertations are elements of
the scientific space (SS). At the same time, the SCS is a structural element of the social space and can
be considered as a complex, poorly structured system. The NP reflects the interaction of objects
related to science. Streamlining this interaction, structuring different areas of the NP will contribute to
a better understanding of it, increase its manageability and progressive development.
Today, researchers do not have an unambiguous and adequate understanding of the NP, which is
part of the social space, along with religious, legal, moral, aesthetic, and philosophical spaces. The
parameters of the NP fix the place, direction, depth and extent of the activities of social actors aimed
at the scientific development of the environment [12]. In general, the concept of "scientific space"
expresses the place of science in culture, outlines and localises its properties as a specific field.
Since NP is a complex multidimensional phenomenon, it is impossible to give a single definition
of this concept, and therefore there are several definitions of NP [13]. It is customary to distinguish
between different aspects of the concept of NP [14]: nominal, semantic, syntactic, pragmatic.
There are different approaches to the study of NPs [15, 16]. For example, by analysing research
collaboration networks [17; 18]. Academic degrees and academic titles serve not only to motivate
researchers and research and teaching staff, but also to structure individual components of the SP.
Some tasks of studying the research activities of subjects and objects of the NP are considered in [19-
21]. In the modern world, in the context of globalisation, Ukrainian scientists are obliged to be
integrated into a single information scientific space. Ukrainian science, despite all the losses and
problems, has retained the ability to carry out world-class research in a number of relevant areas.
The study of NPs is relevant, in particular, because today the trend towards further differentiation of
scientific knowledge continues. At the same time, the boundaries of interaction between scientists are
expanding, and the boundaries between different branches of science are increasingly blurred [22, 23].
5. Information support for the study
5.1. Source of data for the study
The structure of the Ukrainian scientific space was modelled on the basis of open data [24] on the
defence of dissertations by Ukrainian scientists in the period from 1999 to 2023 for the group of
specialities 05.13.00 - 05.13.22. The obtained fragment of data on thesis defence contained about
4000 records.
5.2. Preliminary research results
At the preliminary stage of data analysis, a 60x60 adjacency matrix was constructed, where the
diagonal corresponds to theses completed and defended in the same city. It is convenient to visualise
the contents of the adjacency matrix in two graphs (Fig. 1 and Fig. 2), which demonstrate the
geographical location of research centres and the scientific relationships between scientific
institutions in Ukrainian cities.
6. Structure of information on defended dissertations
Based on statistical studies and comparative analysis of dissertations, conclusions can be drawn
about the structure of the NP of Ukraine [25]. Heuristic E1. Dissertations and the fact of their defence
sufficiently reflect the level of scientific research and can be used to structure the NP. It should be
noted that the dissertation is a concentrated, well-structured and sufficiently formalised information
on the results of scientific research. According to official data [10, 11], from 1993 to 2023, more than
130,000 PhD and more than 20,000 doctoral dissertations were defended in Ukraine, and information
on the main attributes of these dissertations is freely available. Heuristic E2. The cities where a
66
significant number of dissertations were written or defended are the leaders in scientific research in
Ukraine.
Figbre 1. Geography of Ukrainian cities where theses were defended (excluding when the work was
completed and defended)
Figure 2. Geography of Ukrainian cities in which theses were written and defended in the same city
There are parameters of the database that contains basic information about theses of Ukrainian
scientists obtained from open sources: Registration number, Date of registration, Title of the thesis,
67
Abstract, Surname, name, patronymic of the author, Name of the group of sciences and academic degree,
Speciality, Field of science and academic degree, Organisation_1, where the thesis was defended,
Ministry, to which Organisation_1 belongs, Organisation_2 in which the thesis was defended, Ministry to
which Organisation_2 belongs, Organisation_3 in which the thesis was defended, Ministry to which
Organisation_3 belongs, Specifics of speciality, UDC, UDC refinement, Date_1, Date_2.
The content of the database is sufficient and can be used as a basis for analysis in this area of
research. Based on the analysis of the cities where theses were defended, measures can be planned to
strengthen cooperation between different regions of Ukraine, coordinate the activities of research
centres, promote interaction between branches of science, etc.
For further analysis, we will use the following parameters ai , i I , the values of which are
available in open access databases:
a1 Title of the thesis;
a 2 Abstract;
a3 Surname, first name, patronymic of the author;
a 4 Name of the group of sciences and academic degree;
a5 Speciality;
a 6 Field of study and academic degree;
a 7 Organisation_1, where the thesis was completed;
a8 The Ministry to which Organisation_1 is subordinated;
a9 Organisation_2, where the thesis was defended;
a10 Ministry to which the Organisation is subordinated_2;
a11 Organisation_3, where the thesis was defended;
a12 Ministry to which the Organisation is subordinated_3;
a13 Specifying the specialty;
a14 UDC;
a15 Clarification of the UDC;
a16 Date of thesis defence.
7. Modelling interconnections between Ukrainian research centres
Heuristic E3. The organisations where several dozen dissertations were completed or defended are
scientific centres of Ukraine.
Let the set n of elements A a1 ,...,an be given. At this stage of research, the elements of the
set A are the cities of Ukraine where theses were written and defended. For further research, we will
introduce sets whose elements will be used to quantify the structuring of NPs:
S 1 a set of interconnections between cities related to the defence of dissertations;
S 2 is the set of all existing relationships between organisations that are related to thesis defence;
S 3 clusters of research centres;
S 4 Dissertation defence centres by speciality;
S 5 the number of dissertations available in the database.
For an in-depth study, we will use the following metrics:
● the sum of inputs and outputs;
● sum of facts - without specifying the number of arcs and cycles;
● number of arcs;
● number of cycles.
2
From the above definitions, it is obvious that the set S of relationships between organisations is a
1
subset of the set S of relationships between cities related to thesis defence. We will model the
structure of Ukraine's NPs as a weighted graph. The vertices of the graph are the cities of Ukraine.
The output vertex is the city in which the thesis was written, and the input vertex is the city in which
68
the thesis was defended. Let us construct a matrix of statistics of dissertation defences B bij ,
1 1
whose elements are generated according to the following rules:
- is the sum of such i for which there exists a path from vertex ai to vertex a j ;
- is the sum of the minus signs of such i for which there exists a path
bij
1
from vertex a j to vertex ai ;
- 0 if the path from vertex a to vertex a does not exist (omit zero values)
i j
ai A1 - is the set of cities in which the theses were defended; a j A2 - is the set of cities in
which theses were defended.
A1 A2 A , A1 A 2 .
To illustrate, we present a matrix of statistics on the relationship between cities, which was
obtained on the basis of an analysis of real (open) data [24] on the defence of dissertations by
Ukrainian scientists in the speciality 05.13.06 "information technology". Based on this information,
we will also build a graph showing the geographical location of research centres and scientific
relationships between scientific institutions in Ukrainian cities. On the basis of the matrix of statistics
on the relationship between scientific organisations in Ukrainian cities, we will build an adjacency
matrix. A graph showing the connections between the cities of Ukraine where theses were defended
and defended and illustrating the statistics of interconnections between cities is shown in Fig. 3.
7.1. Some aspects of researching relationships
In order to structure the NP, the following aspects of interrelationships should be investigated:
● between the organisations where the thesis was completed and the organisations where it was
defended;
● between dissertators, opponents, supervisors or consultants;
● based on the analysis of the keywords in the thesis.
Thus, the object of the study is a set of dissertations defended in Ukraine from 1993 to 2023. And
to determine the relationships between the objects and additional structuring, NPs can be used:
● measures of similarity between objects;
● keyword ranking tasks;
● an estimate of the weighting vectors calculated on the basis of absolute values.
7.2. Modelling the research of dissertation topics
Heuristic E4. The titles of dissertations and keywords sufficiently reflect the subject matter of
dissertation research and can be used to structure the NP.
It is advisable to conduct a study of dissertation topics, determine the degree of similarity in
dissertation titles, word statistics, etc. To do this, we will focus on a specific speciality, for
example, a5 "Computer Science". We will consider the following subsets:
S 51 scientific supervisors / scientific consultants; S 52 scientific opponents; S 53 annotations;
S 54 Keywords.
7.1. Processing natural language information in research of scientific
space
To further develop the research, let us consider one aspect of scientific interconnections. For an in-
depth study of the scientific space of Ukraine, in addition to, for example, a 3 "Author's name,
surname, patronymic", additional information may be used, in particular:
a17 Surname, name, patronymic of the supervisor/consultant;
69
a18 keywords in the titles of dissertations;
a19 keywords by annotations;
a 20 UDC analysis.
Івано-Франківськ
Сєвєродонецьк
Кропивницкий
Хмельницький
Краматорськ
(Краснодон)
Кам'янське
Слов'янськ
Запоріжжя
Маріуполь
Кременчуг
Кривий ріг
Бердянськ
Тернопіль
Сорокине
Миколаїв
Житомир
Алчевськ
Луганськ
Ужгород
Донецьк
Чернівці
Полтава
Черкаси
Чернігів
Вінниця
Польща
Чортків
Херсон
Бахмут
Дніпро
Харків
Одеса
Ізмаїл
Луцьк
Крим
Рівно
Львів
Суми
Київ
Алчевськ 1
Бахмут 2
Бердянськ 1 1 1
Вінниця 30 8 2 1
Дніпро 1 22 8 2
Донецьк 30 2 1 1 5 1
Житомир 1
Запоріжжя 1 2 5
Івано-Франківськ 3 1 1
Ізмаїл 2 1
Кам'янське 1
Київ -5 -1 376 1 2 1 2 1 13 10 9 1
Краматорськ 1
Сорокине
(Краснодон) 1
Кременчук -1 11 4
Кривий ріг -1 -1 1 2 1
Крим -3 6 2 3 1
Кропивницкий -5 7 2
Луганськ -5 -4 -2 2 1
Луцьк -3 2
Львів -23 38 1 1
Маріуполь -2 -1 -1
Миколаїв -2 -3 -1 4 1 6
Одеса -3 83 1 3
Полтава -1 -2 5
Польща -1
Рівно -1
Сєвєродонецьк -3 1 2
Слов'янськ -3
Суми -2 -2 10 1
Тернопіль -2 11
Ужгород -5 -1 -1 1
Харків -1 -3 292 6 2
Херсон -3 -1 -2 75
Хмельницький -9 -3 -5
Черкаси -14 -2 24 2
Чернівці -2 -1 -1
Чернігів -5 8
Чортків -1
Figure 3. Matrix of interrelations between scientific organisations of Ukrainian cities (zero values of
elements are omitted)
In order to fill the database with additional information, a new study should be conducted and the
information missing from other sources should be entered.
Textual data is an attribute of our civilisation: we see it when we read books, newspapers, other
printed materials, search for information on the Internet, use Facebook and Twitter, communicate
with each other on various forums, etc. [26, 27]. The amount of this data is growing exponentially.
Moreover, approximately 80% of text data is unstructured text. These are Wikipedia articles, web
pages, blogs, emails, social media posts, e-books, etc. It is impossible to read and process all of this
textual data, and in order to extract the most useful information from it, it needs to be structured,
ordered, systematised, etc. Thus, there is a need for tools that help people process unstructured texts
more efficiently. Therefore, the involvement of computers in solving such tasks is quite a natural
phenomenon [28].
The ability of computers to perform useful tasks related to human (natural) language, to perform
high-quality text or speech processing, to assist in communication between people who speak
different languages, and, in general, the ability to communicate between humans and machines - all
these problems are being solved by Natural Language Processing (NLP). Today, the main areas of
application include information retrieval, information extraction, machine translation, question and
answer systems, dialogue systems, speech recognition, natural language generation, and text tone
analysis [29, 30].
The main components of the methodology for applying natural language information processing in
research on scientific space can be developed and implemented in several directions.
1. Monitoring of existing measures of similarity between texts and development of new measures
of similarity if necessary.
70
2. Generate text annotations using different approaches and determine the similarity measures
between the generated annotations in order to identify the tools that best generate text annotations in
the selected field of knowledge.
3. Generation of abstracts of theses defended in Ukraine since 1991.
4. Identification of research areas based on the analysis of abstracts of dissertations.
5. Clustering of research areas based on the automatic identification of these areas by abstracts of
dissertations.
6. Generating annotations of dissertations that are in the public domain.
7. Determination of the similarity measures of dissertation annotations made by the dissertator and
automatically generated by different approaches.
8. Construction of membership functions for annotations created by the dissertator based on the
calculated similarity measures with automatically generated annotations.
9. Automation of research on the internationality of scientific events.
10. Classification of publications submitted to the scientific event according to the declared tracks
(sections) of the scientific event.
11. Automatic determination of the level of scientific results and the quality of scientific activity of
a researcher using the formula of the best teacher.
12. Dynamic automatic supplementation of the teacher's scientific performance.
13. Automatic determination of the number of self-citations in scientific texts that are in the public
domain.
14. Building graphs related to mutual citations of different authors in scientific papers.
15. Study of cycles in references graphs in cases of indirect (cyclic) references.
16. Study of the level of cooperation of scientists, i.e. the ratio of the total number of scientific
papers written in co-authorship to the total number of co-authors and the number of published
scientific papers.
Many challenges still exist, but significant progress has been made in the field of natural language
processing in recent years. Today, the maturity of natural language processing is encouraging more
and more companies to use natural language processing in their products or in their internal
organisation [31].
7.2. Description of the approach to finding keywords from
annotations
Heuristic E5. The keywords reflect the content and direction of the dissertation to a sufficient
extent and can be used to determine the similarity of research areas in the works.
Heuristic E6. The degree of similarity between the keyword sets of any two dissertations
sufficiently reflects the similarity of the content of the dissertations (for some research areas and
decision-making situations).
Heuristic E7. The study of the similarity of keyword sets can be applied (used) to identify clusters
of scientific research groups and to identify similarities in research interests of researchers.
The intersections of interests between branches of science based on keywords will be investigated
using decision theory methods [32, 33].
7.3. Experimental research on identifying similarity between
keywords from thesis abstracts
To determine the interrelationships and additional structuring of the NPs, the similarity analysis
between the dissertations was carried out using the following algorithm (Fig. 3). The algorithm (Fig.
4) was implemented in the Orange Data Mining analytical system (open source software). The
widgets of the analytical platform allow the use of visual programming within which analytical
procedures are created by linking certain blocks (widgets). The proposed scheme for determining
similarity distances between dissertations is shown in Figure 5.
In this case, the widgets Corpus, Preprocess Text, Bag of Words, Distances and Distance Matrix
are used.
71
Figure 4. Context diagram of the algorithm for checking similarity between dissertations
The Corpus widget allows you to transform the input text data into a corpus of text documents.
Preprocess Text splits the text into smaller units, filters them, and performs normalisation (stemming,
lemmatisation). Text processing was performed on English-language dissertation annotations. The
Bag of Words widget creates a corpus with the number of words for each instance of the document
data, the count value is set to absolute, and the frequency is set to IDF m, binary (contains or does not
contain) or sublinear (logarithm of the frequency term). The Jaccard metric is selected as the
similarity distance metric in the Distances widget settings. The distances are visualised using the
Distance Matrix widget (Fig. 6). The size of the distance matrix is 3452x3452.
Figure 5. Scheme for determining similarity distances in Orange Data Mining
8. Prospects for further research
For the purpose of a more detailed and comprehensive structuring of the NP, a study of
dissertations in different areas may be carried out in the future:
● in the context of the fields of science;
● taking into account the dynamics of protection over the years;
● correlation of scientific centres in cities or subordination to ministries or fields of science;
● researching doctoral and PhD theses separately;
● conducting research only in the context of completed ( a7 ) and separately - in the context of
defended ( a9 , a11 ) dissertations;
● research of research centres - organisations in cities;
● in-depth analysis of cycles: the relationship between the centres where theses were written
( a7 ) and defended ( , a9 a11 ).
where , ai i 7,9,11 are the cities where the respective organisations are located.
72
Figure 6. A fragment of the distance matrix in Orange Data Mining
By applying and implementing the results of the analysis in practice, the interaction between
research centres can achieve a more efficient management of research processes [34, 35].
It is possible to identify trends in scientific cooperation and predict further cooperation between
scientific schools [36, 37]. It is also possible to improve the level of organisational culture among
scientific organisations, which will contribute to the efficiency of scientific institutions and enhance
synergies [38, 39].
Artificial intelligence methods should also be used to analyse theses defended in Ukraine.
9. Conclusions
The paper examines the concept of scientific space and its peculiarities in Ukraine. The paper
presents approaches to the analysis of dissertations defended in Ukraine in 1993-2020. An analysis of
Ukrainian cities in which theses were written and defended is carried out. These cities are defined as
scientific centres. Thus, a preliminary structuring of the NPs in terms of the preparation and defence
of dissertations was carried out. Models have been developed and directions for further research of
Ukraine's NPs have been identified, in particular, the analysis of dissertation defence in Ukraine and
all possible scientific interconnections between teams of scientists related to the procedures for
dissertation research and dissertation defence.
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