The Taxonomies of Educational and Scientiﬁc Studies Role in

Centralized Informational Web-Oriented Educational Environment

Viktor B. Shapovalov

1 a

Yevhenii B. Shapovalov

1 b

, Roman A. Tarasenko

1 c

Stanislav A. Usenko

1 d

, Adrian Paschke

2 e

and Irina M. Shapovalova

3 f

The National Center “Junior Academy of Sciences of Ukraine”, 38-44 Degtyarivska Str., Kyiv, 04119, Ukraine

Fraunhofer FOKUS (with support of BMBF “EXPAND+ER WB3”), 31 Kaiserin-Augusta-Allee, Berlin, 10589, Germany

Secondary comprehensive school No. 69 of Kyiv, 25 Donetska Str., Kyiv, 03151, Ukraine sjb@man.gov.ua,

Keywords:

Cloud Technologies, Ontology, Educational Research, Taxonomy, Systematization.

Abstract:

The scientiﬁc/educational studies may be structured using the formalization of IMRAD approach that pro-

vides interoperability of data. The study focusing on the using of the studies’ results as part of the centralized

informational web-oriented educational environment. The structurization was provided on two studies – “De-

velopment of a rational approach for utilizing methane tank waste at LLC Vasylkivska chicken farm” and

“Development of a strategy for utilizing methane tank efﬂuent”. The speciﬁc tools of CIT Polyhedron were

used to make speciﬁc tools related to processing of studies data. The audit tool provides comparing the newly

inputted data to existing data in taxonomies and highlights the cases of full corresponding of some elements of

works for existing ones (for example, objects of studies). The approach of integration of studies with educa-

tional ontologies (that is part of centralized informational web-oriented educational environment) is described.

The formalization of this process is described using mathematical expressions.

1 INTRODUCTION

Now, more than ever, science affects all aspects of hu-

man life. The latest scientiﬁc developments are often

and quickly implemented in the industry. However,

the scientiﬁc results are usually presented in human-

readable form and not in a machine-readable format,

so it is hard to process the knowledge using automated

informational technologies.

The basic structure of a typical research paper is

the sequence of Introduction, Methods, Results, and

Discussion (sometimes noted as IMRAD) (Oriokot

et al., 2011). Each section addresses a different ob-

jective. For example, the Introduction section moti-

vates the research problem that was discovered or the

known facts about the problem; the Method section

states what authors did to learn and address the issue

in a new solution, and what they achieved as results

in experiments is written in the Discussion section,

https://orcid.org/0000-0001-6315-649X

https://orcid.org/0000-0003-3732-9486

https://orcid.org/0000-0001-5834-5069

https://orcid.org/0000-0002-0440-928X

https://orcid.org/0000-0003-3156-9040

and what they had observed is discussed in the Re-

sults section.

The most common form of science reporting is a

written paper. Depending on the purpose, there are a

few different types of papers: Analytical Research Pa-

per, Argumentative (Persuasive) Research Paper, Def-

inition Paper, Compare and Contrast Paper, Cause and

Effect Paper, and Interpretative.

The most common research papers types are

shown in table 1 (Paperpile, 2019).

Nowadays, most of the papers (but not all of them)

are systemized by using scientometric databases.

However, educational research reports, which use sci-

entiﬁc methods, have not been systemized. Unlike

pupils, scientists already know their ﬁeld of research

in detail and can determine their research hypothesis,

and they can do further analysis by themselves. Stu-

dents, instead, can’t do this. Automated informational

tools can help students in this scientiﬁc discovery and

analysis tasks.

The STEM (Science, Technology, Engineering

and Math) may be interpreted as using of the sci-

entiﬁc method in an educational process while pro-

viding academic research. This approach is only re-

cently applied in countries such as Ukraine. There

are various school competitions for scientiﬁc works,

128

Shapovalov, V., Shapovalov, Y., Tarasenko, R., Usenko, S., Paschke, A. and Shapovalova, I.

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment.

DOI: 10.5220/0012062100003431

In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology (AET 2021), pages 128-143

ISBN: 978-989-758-662-0

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

Table 1: The most common research papers types.

Types of the Research

papers

Oriented amount of

words required

Speciﬁc characteristics

Analytical Research Pa-

per

3000+ Someone poses a question and then collect relevant data

from other researchers to analyse their different view-

points.

Argumentative (Persua-

sive) Research Paper

3000+ The argumentative paper presents two sides of a contro-

versial question in one paper.

Deﬁnition Paper 5000+ The deﬁnition paper describes facts or objective argu-

ments without using any personal emotion or opinion of

the author.

Compare and Contrast

Paper

5000+ Compare and contrast papers are used to analyse the dif-

ference between two viewpoints, authors, subjects or sto-

ries.

Cause and Effect Paper 3000+ Cause and Effect Paper trace probable or expected re-

sults from a speciﬁc action and answer the main questions

“Why?” and “What?”.

Interpretative Paper 3000+* An interpretative paper requires to use knowledge that

have gained from a particular case study.

Experimental Research

Paper

3000+* This type of research paper describes a particular experi-

ment in detail.

Survey Research Paper 5000+* This research paper demands the conduction of a survey

that includes asking questions to respondents.

* Depends on the purpose of the article and the requirements of the journal, institute, teacher.

such as the competition on scientiﬁc articles of the Ju-

nior academy of sciences of Ukraine and international

competitions (Intel ISEF). Also, the scientiﬁc method

can be used during the creation of thesis papers (for

masters’ degrees, bachelor’s degrees, etc.) and pupil’s

research reports. (for events noted before), or in sim-

pler, but more common form of essays. In addition,

students can report their results in scientiﬁc papers if

the quality of their work is satisfactory for the scien-

tiﬁc requirements. An overview of the types of educa-

tional research reports works is presented in table 2.

This paper focuses on the systematization and pro-

cessing of academic research reports. The problem to

be addressed is the lack of a structuring mechanism

that complicates the automated processing of such re-

ports.

2 LITERATURE REVIEW

The active dissemination and use of different sci-

entometrics databases continue to increase the con-

venience and efﬁciency of scientiﬁc data process-

ing, structuring, and systematization of research

and scientiﬁc results. Specialized databases for

structural science information are an integral part

of the information-support system for any scien-

tist. Scientometrics is the “quantitative study of sci-

ence, communication in science, and science pol-

icy” (Ramesh Babu and Singh, 1998), commonly re-

ferred to as the “science of science”. Scientomet-

rics is essential to help academic disciplines under-

stand various aspects of their research efforts, in-

cluding (but not limited to) the productivity of their

scholars (Ramesh Babu and Singh, 1998; Abramo

et al., 2011), the emergence of specializations (Pianta

and Archibugi, 1991), collaborative networks (New-

man, 2001), patterns of scientiﬁc communications

(Braun et al., 2001), and quality of research products

(Lawani, 1986). Metric studies have been developed

as a subsidiary branch of Library and Information Sci-

ence (LIS) (Khasseh et al., 2017). Often, scientomet-

rics applies bibliometrics, which measures the impact

of publications.

To increase the quality and performance of scien-

tometrics the ten principles of the “Leiden Manifesto

of Scientometrics” have been stated:

1. Quantitative evaluation should support qualitative

expert assessment.

2. Measure performance against the research mis-

sions of the institution, group, or researcher.

3. Protect excellence in locally relevant research.

4. Keep data collection and analytical processes

open, transparent and simple.

5. Allow those evaluated to verify data and analysis.

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

129

Table 2: Types of the educational research reports.

Types of the edu-

cational research

report

Oriented required

amount of the pages

Speciﬁc characteristics The event for which the re-

port was prepared

Esse In general, up to 10-

15 pages

Is simple and very ﬂexible

on the content

Classes, completions of

school level

Research reports In general, up to 30-

100 pages

Relatively static structure;

similar to IMRAD

Competitions of Junior

academy of sciences of

Ukraine and Intel ISEF

Scientiﬁc paper Declared by the

source

Declared by the source Publication in the journal

Thesis papers In general, 40-100

pages

Relatively static structure

similar to IMRAD

Defence of the qualiﬁcation

works

6. Account for variation by ﬁeld in publication and

citation practices.

7. Assessment of individual research on a qualitative

judgment of their portfolio.

8. Avoid misplaced concreteness and false precision.

9. Recognize the systemic effects of assessment and

indicators.

10. Scrutinize indicators regularly and update them

(Khasseh et al., 2017).

Today, all existing scientometrics databases can be

divided into two major groups: international and na-

tional (Khasseh et al., 2017; Kostenko et al., 2015;

Mulla, 2012; Ravikumar et al., 2015; Pavlovskiy,

2017; Perron et al., 2017; Ram

ırez and Rodr

ıguez

Devesa, 2019). The most well-known interna-

tional databases are Springer, Scopus, Web of Sci-

ence, CiteseerX, Microsoft Academic, aminer, ref-

seek, BASE (Bielefeld Academic Search Engine),

WorldWideSciense, JURN, Google Scholar, Google

Patents, and others. National databases incorporate a

variety of bibliographic databases and a variety of li-

brary and university repositories. International scien-

tometric databases are characterized by a larger scale

and mandatory support for various languages, includ-

ing English. Also, a characteristic feature of such

databases is the availability and work with multiple

unique indices that have international recognition, for

example, the h-index (Kinouchi et al., 2018).

As scientiﬁc publications continue to grow expo-

nentially, the number of academic databases and sci-

entometrics databases increases, which supports gain-

ing insights into the structure and processes of sci-

ence (Perron et al., 2017). In this case, many sci-

entiﬁc publications are devoted to the principle of

working scientometrics databases, and their number

is growing. Thanks to them, concepts such as “meta-

data” of scientiﬁc articles began to be actively used

in scientometrics (Khasseh et al., 2017; Kostenko

et al., 2015; Mulla, 2012; Ravikumar et al., 2015;

Pavlovskiy, 2017; Perron et al., 2017; Ram

ırez and

Rodr

ıguez Devesa, 2019). Metadata is essential data

about data providing information such as titles, au-

thors, abstracts, keywords, cited references, sources,

bibliography, and other data. Metadata does not sub-

stitute the corresponding article, but it explicitly de-

scribes valuable information about the report.

By using scientometrics systems, researchers’

contributions in informatics and scientometrics were

previously quantiﬁed (Mulla, 2012). The principal

metadata indicators are:

• The indicators and citation indices of journals.

• The number of authors.

• The number of publications.

• The degree of cooperation is based on afﬁliation

data.

The disadvantage of this research is that it is de-

voted only to scientiﬁc articles. The authors noted

that their study could not cover students’ and pupils’

research reports because there is no single database

where they are all located.

The application of the principles of the “Leiden

Manifesto of Scientometrics” is stated and substanti-

ated, providing transparent monitoring and support of

research and encouraging constructive dialogue be-

tween the scientiﬁc community and the public. In

this work, the bibliometric base, which corresponds

to principles of the “Leiden Manifesto of Scientomet-

rics”, has been created. The proposed bibliometric

center did not address the systematization of students’

and pupils’ research reports. Still, the authors noted

the necessity of involvement of students’ and pupils’

research reports in their bibliometric center.

The approach of co-word analysis has been intro-

duced, and its application in scientometrics is sub-

stantiated in (Ravikumar et al., 2015). The trends

and patterns of scientometrics in journals has been re-

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

130

vealed by measuring the association strength of se-

lected keywords which represent the produced con-

cept and idea in the ﬁeld of scientometrics. Also, the

authors have developed a web system for extraction

of keywords from the title and abstract of the arti-

cle manually. However, the web system proposed by

them cannot work with research reports of students

and pupils.

Another concept of analysis is iMetrics, or “in-

formation metrics”. Its application in scientomet-

rics is substantiated in (Milojevi

c and Leydesdorff,

2013). iMetrics is devoted to the scientometrics of

scientiﬁc journals in the ﬁeld of informatics. The au-

thors note the possibility of applying their approach

to the systematization of the scientiﬁc works of stu-

dents and pupils. The research related to scientomet-

rics databases is shown in table 3.

Table 3: Researche related to scientometrics databases.

Subject of study The general result of the au-

thors study

Citation indices

of journals, num-

ber of authors of

the publication

their afﬁliation

The contributions of re-

searchers in the ﬁeld of in-

formatics and scientometrics

(Mulla, 2012)

Principles of

the “Leiden

Manifesto of

Scientometrics”

Stated and substantiated

“Leiden Manifesto of Scien-

tometrics” (Kostenko et al.,

2015)

Co-word analysis The trends and patterns of

scientometrics in the jour-

nals were revealed (Raviku-

mar et al., 2015)

iMetrics (“infor-

mation metrics”)

iMetrics scientometric

system has been provided

(Milojevi

c and Leydesdorff,

2013)

Previously, ontological graphs were used to sys-

tematize scientiﬁc articles (Amami et al., 2017;

Boughareb et al., 2020; Perraudin, 2017; Parveen,

2018). Systematization and structuring in such

graphs are based on different approaches, such as

using of scientiﬁc article recommendation system

(Amami et al., 2017), Scientiﬁc Articles Tagging

system (Boughareb et al., 2020), machine learn-

ing (Perraudin, 2017), and automatic summarization

(Parveen, 2018). Also, ontologies can be used to pro-

vide interoperability through semantic technologies

(Alnemr et al., 2010). However, none of the proposed

ontological approaches for systematization and struc-

turing addresses the structuring of research reports of

students and pupils.

None of the scientometrics database systems pre-

viously proposed (Khasseh et al., 2017; Kostenko

et al., 2015; Mulla, 2012; Ravikumar et al., 2015;

Pavlovskiy, 2017; Perron et al., 2017; Ram

ırez and

Rodr

ıguez Devesa, 2019) can offer a universal solu-

tion for systematization and structured presentation of

research and scientiﬁc results to pupils and students.

Also, the disadvantages of all these systems are the

complete lack of many valuable parameters for pro-

cessing information about scientiﬁc works. These pa-

rameters are the scientiﬁc novelty of the article, the

practical value of the study, the hypothesis of the

study, subject and object of the research. Also, ex-

isting solutions do not allow for comparing the meta

data about the research reports between each other.

This work aims to propose and justify using an on-

tological system, which permits the systematization

of scientiﬁc articles with all advantages of existing

scientometrics systems and without disadvantages of

these systems. Which at the same time will not be de-

prived of the functionality of current scientometrics

systems and will meet the Leiden Manifesto for Sci-

entometrics.

As Proof of Concept (PoC) we propose to use

the existing cognitive IT platform Polyhedron as the

technical basis for solving this problem. The core

of the Polyhedron system consists of advanced and

improved functions of the TODOS IT platform de-

scribed in previous works. The polyhedron is a multi-

agent system that allows for transdisciplinary and acts

as an interactive component in educational and scien-

tiﬁc research (Stryzhak et al., 2014). Besides, the cog-

nitive IT platform Polyhedron contains a function for

comparison with standards which is called auditing

(Stryzhak et al., 2014; Globa et al., 2015, 2019). Poly-

hedron provides: semantic web support, information

systematization and ranking (Stryzhak et al., 2021),

transdisciplinary support, and internal search (Shapo-

valov et al., 2019), has all advantages of ontological

interface tools (Popova and Stryzhak, 2013), and the

construction of all chains of the process of transdis-

ciplinary integrated interaction is ensured (Velychko

et al., 2017). Due to active states for hyper-ratio

plural partial ordering (Volckmann, 2007; Nicolescu,

2008), the cognitive IT platform Polyhedron is an in-

novative IT technology for ontological management

of knowledge and information resources, regardless

of the standards of their creation. The user of the

Polyhedron IT system has an opportunity to use an

internal search function that has more views than the

external one because it provides information created

by experts.

Also, the proposed solution for the structuring

of educational and research projects can be used to-

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

131

gether with other modern developments in the aca-

demic ﬁeld, like a virtual educational experiment

(Slipukhina et al., 2019), different tools to provide

development of ICT (Modlo et al., 2018), the use

of mobile Internet devices (Modlo et al., 2019), us-

ing the technology of augmented reality education

(Bilyk et al., 2022), online courses (Vlasenko et al.,

2020; Yahupov et al., 2020), distance learning in vo-

cational education and training institutions (Modlo

et al., 2019), educational and scientiﬁc environments

(Shapovalov et al., 2019).

As was investigated before, the main elements of

educational studies are represented by IMRAD nodes

and their speciﬁc subnodes related to a particular

study (Shapovalov et al., 2022). They may be de-

scribed by a set of formulas. According to the the-

ory of using IMRAD, each examination consists of an

Introduction, Methods, Results, and Discussion (that

in terms of informational systems, the discussion is

charged to processing – P):

{I, M, R, P} ∈ S (1)

where I – node of ontology that integrates data related

to introduction; M – subject of study: node of ontol-

ogy that integrates data related to methods; R – node

of ontology that integrates data related to results; P –

results of study’s results processing.

Each scientiﬁc study contains speciﬁc data struc-

tured by IMRAD, and it may be represented as a set

of tuples (corteges) that describe elements of speciﬁc

studies. The equations 2 and 3 are used to describe

representing two different studies structured by IM-

RAD:

= <I

, M

, R

, P

> (2)

= <I

, M

, R

, P

> (3)

Two different studies integrated into a single on-

tology will be described as the sum of IMRAD ele-

ments. Such representation is shown in equation 4:

, S

i = hI

, M

, R

, P

, I

, M

, R

, P

i (4)

In such case, some speciﬁc elements of studies

are overlapping and other are not. For example, the

Method section of two different studies represented in

form of an ontology using IMRAD will be as follows:

= M

, M

(5)

= M

, M

(6)

In such representation M

and M

belong to both

studies, and it is possible to use them as linking nodes

to connect the two studies:

∈ M

, M

(7)

∈ M

, M

(8)

It is worth noting that such representation of stud-

ies leads to the ontologization of the studies’ data.

The most speciﬁc terms may be used to connect to

different types of ontology-based knowledge, for ex-

ample, educational programs. However, such an ap-

proach was not conducted before. This study also

aims to provide interoperability between different

ontology-based knowledge systems using terms used

in conducted studies and other knowledge systems

(on the example of educational systems).

3 MATERIALS AND METHODS

3.1 Ontology Creation Mechanism

To create ontologies in Polyhedron, Google Sheets

were used to collect by expert who took the data man-

ually and structuring the information (see example in

ﬁgure 1). The sheets with research report data (struc-

ture ﬁle and numeric/semantic data ﬁle) have been

downloaded and saved in .xls format. The ﬁles have

been loaded to “editor.stemua.science”, part of Poly-

hedron. After that, the generation of the graph nodes

(in .xls) with their characteristics using the data struc-

tures in the ﬁle has been carried out. The obtained

graphs have been saved in .xml format and located in

the database. The graphs have been ﬁlled with seman-

tic and numeric information for ranking and ﬁltering.

Ontological edges (relations) have been formed using

predicate equations, as described previously in (Vely-

chko et al., 2017).

3.2 Ranking Tools

Considering that, e.g., proposed reports “A” and “B”

are technical, the results of the reported works can

be used to analyze the rationality of the implementa-

tion proposed in the concrete project. For instance, to

offer it, research reports “A” and “B” were also com-

pared with each other using a ranking tool applying

the following criteria: “Short-term economic perspec-

tive”, “Long-term economic prospects”. For creating

a ranking, the ontologies have used the module “Al-

ternative”, described in (Stryzhak et al., 2021). The

nodes of a graph have been ﬁlled with semantic data

to provide this ranking.

The ranking uses a grade scale from one to ten

points to underline the relevance coefﬁcient. The

projects with a payback period of more than 25 years

have been evaluated with 1 point, with 20-25 years

of payback period with 2 points, from 15-20 years

of payback period with 3 points, from 10-15 years

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

132

Figure 1: Google sheet with data.

of payback period with 4 points, 6-10 years of pay-

back period with 5 points and with 1-5 years were

evaluated as 6-10 points, respectively, by the “Eco-

nomic attractiveness” criterion. A detailed evaluation

for projects with 1-5 years is provided due to its ut-

most interest for the investor’s “payback time”, which

determines investment expediency.

3.3 Auditing Tools

To provide an audit of the hypothesis of work “A”

and “B”, the “standard” graph (with which the com-

parison is done) and the “comparison” graph (which

is compared with the “standard”) have been created.

The “standard” ontology graph contains the data on

hypotheses, subjects, objects of research, keywords,

and other parameters of the research reports done be-

fore. For the “standard” graph, each parameter was

presented in a separate node. The content of this on-

tological graph “standard” is constantly updated and

supplemented.

The nodes of the “comparison” graph have been

represented as names of the works which need to be

audited with the “standard” graph. The parameters of

the work used to be audited with the “standard” graph

have been located in the metadata of each separate

node. The metadata type names were identical to the

terms of the nodes of the “standard” graph to enable

interaction between graphs.

3.4 Using Centralized Informational

Web-Oriented Educational

Environment Concept and Ensuring

Interdisciplinarity

The developed ontologies were saved in the same en-

vironment where elements of the centralized infor-

mational web-oriented educational environment were

saved. Its features were used to provide interoperabil-

ity with educational programs, methods, equipment,

ontology-based didactical materials, and other ontol-

ogy tools. As all such ontologies had the same graphs’

nodes names, we provided the integration between el-

ements of the centralized informational web-oriented

educational environment and proposed structurization

of academic studies. We used the same nodes and

provided links with each graph that it contains. For

example, the term temperature regime that is used in

educational programs is connected with all academic

programs in physics (part of topic energy, thermal

energy), chemistry (amount of topic energy of reac-

tion), and scientiﬁc study graph that was conducted

by young researcher on biogas production research

called temperature regime. Also, we can link this term

with a method called ensuring of requested tempera-

ture by a thermostat and with equipment dry air ther-

mostat. So, for this, we are using the term tempera-

ture regime to provide an interdisciplinarity approach

that is related to different ﬁelds of science and to vary-

ing types of data (educational plans, equipment that is

used, speciﬁc methods and speciﬁc personal studies).

4 RESULTS AND DISCUSSION

The general concept of the proposed ontology-based

graph model for Polyhedron research reports has a

speciﬁc, logically connected structure and can be rep-

resented as an ontology. After structuring, it is pos-

sible to describe the reports’ content in simpler to

understand presentation form. Besides, most results

can be domain-speciﬁc for each industry, and if the

current standards are correctly identiﬁed, these val-

ues will be easy to compare. Also, most research in

one ﬁeld often uses the same equipment, materials,

chemicals, standard methods of analysis, literature,

etc., which allow comparing these works with each

other and correctly structuring them.

However, the main advantage of the proposed ap-

proach (besides structuring the research) is process-

ing results in terms of separated result parameters of

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

133

the reports. This supports data analysis, further pro-

cessing using ranking, and semantic data interoper-

ability. The separation of numeric data and its loca-

tion metadata class is possible due to the addresses

of the same ﬁeld, describing the process using the

same (or similar) parameters of the process descrip-

tion and result parameters description. For example,

for most reports on anaerobic digestion, the process

parameters are temperature, type of substrate, reactor

volume, moisture content, initial pH, parameters; the

characteristics of efﬁciency of the process are biogas

yield, methane content, average pH during the pro-

cess, destruction process, etc. (Zhadan et al., 2021).

As all research reports will be simpliﬁed, this

approach will be especially relevant for pupils and

novice researchers with further potential use in the ed-

ucational process or to streamline the literature review

process for the new academic research.

4.1 Description of Scientiﬁc Works

Used to Provide Structuring

For example, the object of the study of research re-

port “A” is the disposal of anaerobic efﬂuent. The

subject of the report’s research is the Cultivation of

Chlorella Vulgaris microalgae on efﬂuent obtained af-

ter methane fermentation. The study aims to develop

a method of growing Chlorella Vulgaris in the efﬂu-

ent after methane fermentation. The practical signiﬁ-

cance of this scientiﬁc work is the results, which will

contribute to the spread of biogas technologies. Also,

the proposed approach makes it possible to increase

the economic beneﬁts of utilizing chicken manure by

converting the anaerobic digestion efﬂuent into mi-

croalgae with a wide range of applications. The scien-

tiﬁc novelty of that research report is a method of uti-

lization of anaerobic digestion efﬂuent by using mi-

croalgae, also had obtained cultures of Chlorella Vul-

garis that had adapted to the anaerobic digestion ef-

ﬂuent. The working hypothesis was that the efﬂuent

obtained after anaerobic digestion could be used as a

nutrient medium for microalgae Chlorella Vulgaris.

The object of the research report “B” study is the

disposal of anaerobic digestion efﬂuent. The subject

of the research is the processing of anaerobic diges-

tion efﬂuent into humates by the autocatalytic catal-

ysis method. The study aims to establish regulari-

ties of processing the solid fraction obtained during

the methane fermentation of chicken manure by the

autocatalytic catalysis method. The practical signiﬁ-

cance of this scientiﬁc work is that the study indicates

the possibility of acquiring salts of humic and ful-

vic acids by the autocatalytic catalysis method. This

approach makes it possible to increase the economic

beneﬁts of chicken manure disposal by converting

the anaerobic digestion efﬂuent into a more valuable

product with a wide range of applications. Its sci-

entiﬁc novelty is that potassium humate had ﬁrstly

obtained from anaerobic digestion efﬂuent. For the

ﬁrst time, the efﬁciency of receiving humates from the

solid fraction of anaerobic digestion was investigated,

and the main regularities of the process were deter-

mined. The working hypothesis was that the solid

fraction of methane fermentation of chicken manure

can be recycled by the autocatalytic catalysis method.

(a)

(b)

Figure 2: The general view of the (a) research report “A”

(b) research report “B” ontological graph.

For both research reports, “A” and “B”, as a sub-

strate for anaerobic digestion, have used the chicken

manure from the same poultry farm. In this case,

chicken manure and its efﬂuent, which has been ob-

tained by anaerobic digestion, were analyzed by the

same methods and indicators. Such indicators were:

• “Ash and dry content”.

• “Determination of volatile fatty acids content” (in

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

134

terms of acetic acid).

• “Determination of ammonium nitrogen content

with Nessler’s reagent”.

The equipment used to determine these indicators

was also the same. Therefore, how these works can be

structured and integrated using the cognitive IT plat-

form Polyhedron has been considered. All examples

of ontological nodes in the obtained graphs for fur-

ther potential information processing are presented in

table 4.

(a)

(b)

Figure 3: The general view of a) research report “A” b)

research report “B” “Materials and methods” node.

4.2 Structuring of the Scientiﬁc Works

Using Ontologies

To present possibilities and systematization of the re-

search report, we have applied an ontological taxon-

omy for students’ works “A” and “B”. The general

view of the obtained graphs is shown in ﬁgure 3 (Ve-

lychko et al., 2017).

A separate node called “Abstract” has been cre-

ated, which contains all the necessary metadata of

the work, such as “Object of the study”, “Subject

of study”, “The aim of the study”, “Practical value”,

“Scientiﬁc novelty”, “Keywords” and “Hypothesis of

scientiﬁc works” in the form of the attributes. All

metadata has been used to provide ﬁltering and rank-

ing.

The “Materials and methods” node, which con-

tains all the materials, was used to perform the ex-

periments. Every approach has been divided into the

separate attribute of the node. This allows concen-

trating the reader’s attention, and it helps to process

the data with each other. For further researchers, this

mechanism will be described in detail. The general

view of both works’ “Material and Methods” node is

shown in Figure 3 (Velychko et al., 2017).

For each ontological node that duplicates sections

of the research report, and that contain speciﬁc indi-

cators after analysing, additional separate leaf nodes

with these results have been created.

In this leaf node, all the issues are held in the form

of semantic and numeric data. These results are auto-

matically available for ﬁltering, auditing and ranking.

An example of this leaf node is shown in Figure 4.

Figure 4: An example of leaf node with indicators after an-

alyzing.

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

135

Table 4: Examples of the usage of the educational research element in ontology.

Element

of the ed-

ucational

research

Example The role of the node in

the resulting graph

Using of the data

Title Node: “Development a method for

utilization of anaerobic digestion

efﬂuent”

Parent node Used only for structuration

Object Node: Abstract

Class: Object

(object is only one per report)

Value: Anaerobic digestion;

Value: Microalgae’s growth

Value: Disposal of the waste

Located in Abstract

node; each object pre-

sented as attribute

Used for the audit; to provide

literature review; to link reports

for each other with same data; to

identify novelty and plagiarism

Subject Node: Abstract

Class: Subject

Value: The processing of anaero-

bic digestion efﬂuent into humates

by the autocatalysis method

Located in Abstract

node; each object pre-

sented as attribute

Same as previous

Hypothesis Node: Abstract

Class: Hypothesis

Value: Efﬂuent obtained after

anaerobic digestion can be used as

a nutrient medium for microalgae

Chlorella Vulgaris

Located in Abstract

node; each object pre-

sented as attribute

Same as previous

Keywords Node: Abstract

Class: Keywords

Value1: Biogas;

Value2: Anaerobic digestion

Value3: Microalgae

Located in Abstract

node; each object pre-

sented as attribute

Same as previous

Sections, Ab-

stract, Intro-

duction

Node: Introduction;

Class1: Text;

Value1: text itself;

Class2: Biogas production in liter-

ature, ml/g of VS;

Value2: 368;

Class3: methane content, % ;

Value3: 59

Each section presented

in separated nodes; all

text is presented in sep-

arate class of metadata,

based on type of data

Used for representing of the

main text of the educational re-

ports; structuration and naviga-

tion

Materials

and methods

Node: Materials and methods

Class1: Method1;

Value1: Desorption1;

Class2: Method2;

Value2: Desorption2

Located single node;

each method is separated

class of metadata

Used to provide links between

the reports used same method

by indexing and search

Concrete

results and

parameters

of the re-

Node: Results

Class1: pH;

Value1: 7.3;

Class2: Decomposition, %;

Value2: 87

Located a in separate

node; each parameter is

separated class of meta-

data

Used for the creation of the sin-

gle ranking tool to systemize re-

sults from same ﬁeld

Economic

data

Node:Economic data Class: Pay-

back period, years;

Value: 5.3

Located the separate

node; payback period

presented in metadata

Used to provide comparison of

the approaches to assess invest-

ment attractiveness

References Node: Li et al. 2018, Chen 2003,

Sergienko et al. 2016

Each report (paper) lo-

cated in separate node

Used to link reports used same

reference with each other

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

136

5 INFORMATION PROCESSING

OF THE RESEARCH REPORT

USING POLYHEDRON TOOLS

5.1 Using an Audit Tool to Test a

Hypothesis

The audit tool (Stryzhak et al., 2014; Globa et al.,

2015, 2019) can be used to compare the hypotheses,

subjects, objects of research, keywords, and other pa-

rameters of the research reports. To demonstrate the

capabilities of the audit tool, the focus is on auditing

only hypotheses. “A” model version of the “standard”

ontology has been created, which contains metadata

from the “Abstract” node of the research reports “A”

ontological graph. This ontology had a simple struc-

ture without branches, with the parent node being

named “Abstract”. The child nodes duplicate meta-

data from the “Abstract” node of the research reports

“A”.

The “comparison” ontology has been created with

the child nodes, which contain the following hypoth-

esis: the efﬂuent obtained after anaerobic digestion

can be used as a nutrient medium for microalgae Spir-

ulina Platensis (hypothesis 1), and the efﬂuent ob-

tained after anaerobic digestion can be used as a nu-

trient medium for microalgae Chlorella Vulgaris (hy-

pothesis 2), the efﬂuent obtained after anaerobic di-

gestion cannot use it as a nutrient medium for mi-

croalgae Chlorella Vulgaris (hypothesis 3). The hy-

pothesis 2 node also contains some metadata. This

ontology also had a simple structure without branches

with the parent node, the “Hypothesis test system”.

The general view of the obtained ontology of the com-

parison and the ontology of the standard in taxonomic

form is shown in ﬁgure 5.

The system has checked whether the hypothesis is

true or false by using the audit function. Those indi-

cators which do not correspond to the standard have

been colored red. Thus, this solution will allow to

test the idea of these scientiﬁc works and check other

metadata that have already been set by using informa-

tion from the “Abstract” node (ﬁgure 5b).

5.2 Analysing of the Research Reports

Result on the Practice Value

Research report “A” and research report “B” have

been compared with each other by the following cri-

teria “Short-term economic perspective”, “Long-term

economic prospects”. According to section 2 of the

research report “A”, the payback period of project “A”

is ﬁve years, which corresponds to 6 points according

(a)

(b)

Figure 5: General view of in the taxonomic form the on-

tology of the comparing” (a) and (b) the ontology of the

“standard”.

to the criterion “Economic attractiveness”. This pa-

rameter is better for the project described in report

“B” with a payback period of four years and three

months, which corresponds to 5 points on “Economic

attractiveness”. The system provides raking of the re-

sults. If there is a large amount of data, the instrument

will be helpful to quickly and effectively evaluate the

projects on “Economic attractiveness”. Besides, in

further research, the other criteria will be justiﬁed and

used to provide data management on the educational

research, which will make the tool more functional.

5.3 The Role of Taxonomies of

Educational Studies in Centralized

Informational Web-Oriented

Educational Environment

5.3.1 Mathematical Interoperation of

Integration of Taxonomies of Educational

Studies in Centralized Informational

Web-Oriented Educational Environment

As it was shown before, the preleaf nodes (L4) are

terms (main ontology elements) that are very promis-

ing to use in terms of interoperability with other on-

tologies of subject areas, for example, with educa-

tional programs that in that term will be the addi-

tional instrument for Centralized informational web-

oriented educational environment.

So, such connection with the centralized informa-

tional web-oriented educational environment concept

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

137

Figure 6: General view of the audit results in the “Hypothesis test system” ontology.

Figure 7: General view of the ranking result.

and ensuring interdisciplinarity is described by for-

mulas. Each ontology is based on the conceptualiza-

tion of terms. It means that each ontology is described

as a tuple (cortege) of terms from the ﬁeld it contains:

=< t

> (9)

So, we can describe ontology of educational pro-

gram, ontology of equipment that being used, ontol-

ogy of method and ontology of educational studies as

further:

= t

(10)

= t

(11)

= t

(12)

= t

(13)

As seen for equations, some ontologies have

cross-terms that will provide inoperability with CI-

WOEE and interdisciplinary. The terms that are

cross-terms will be used by the user to transfer from

elements (nodes) of one ontology to aspects of an-

other. For this example, leaf or sub-leaf (in the case of

educational studies’ ontology; such as speciﬁc meth-

ods, keywords, objects, etc.), t

, t

, and t

are cross-

terms:

∈ O

, O

(14)

∈ O

, O

(15)

∈ O

, O

(16)

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

138

Figure 8: Terms of studies that may be used to link ontology of scientiﬁc studies with ontology of educational programs.

Figure 9: The general view of educational programs’ ontology related to chemistry educational programs in Ukraine and

terms that may be used to link with ontology of scientiﬁc studies.

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

139

Figure 10: Using of same terms to provide interoperability between educational programs ontology and scientiﬁc studies

ontologies.

Figure 11: Workﬂow diagram of the creation of structured ontologies on scientiﬁc reports and their processing.

5.3.2 Practical Application of Ontology-Based

Integration of Scientiﬁc Studies

Educational Programs of Centralized

Informational Web-Oriented Educational

Environment

As shown in equations 10-13, the terms of scientiﬁc

study ontology, such as speciﬁc methods, keywords,

objects, etc., are used to provide a link with terms of

educational programs. Terms of studies that may be

used to link the ontology of scientiﬁc studies with the

ontology of educational programs are shown in ﬁg-

ure 8.

A similar situation is also related to educational

programs. There is an ontology that systemizes the

data on the knowledge ﬁeld related to chemistry us-

ing schools’ educational programs. It also consists of

terms that are presented in the form of nodes. The

general view of academic programs’ ontology related

to chemistry educational programs in Ukraine and

phrases that may be used to link with the ontology

of scientiﬁc studies is shown in ﬁgure 9.

Therefore, some terms may be related to both edu-

cational programs and scientiﬁc studies ontologies. In

this case, such links allow using subnodes (keywords,

methods, etc.) to ﬁnd scientiﬁc studies related to this

term. The exact words to provide interoperability be-

tween educational programs ontology and scientiﬁc

studies ontologies are shown in ﬁgure 10. As can be

seen, the terms “methane” and “biogas” are related to

both educational programs and scientiﬁc studies and,

therefore, they are used to link these ontologies.

6 DISCUSSION

The proposed database follows the “Leiden Mani-

festo of Scientometrics.” In the obtained ontological

database, quantitative evaluation can be supported by

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

140

qualitative expert assessment. Additionally, this on-

tological database can unite the research missions of

the institution, group, or researcher and protect excel-

lence in internally relevant research. The ontological

form of research reports can keep data collection and

analytical processes open, transparent, and straight-

forward. Because all metadata is contained in a sep-

arate node that can be expanded and supplemented.

Thus, the obtained ontological database can also ac-

count for variations, e.g., in publication and citation

practices. It can provide a base assessment of individ-

ual researchers’ qualitative judgment of their portfo-

lios. Because all ontological graphs are validated by

experts, in this way, it is possible to avoid misplaced

concreteness, including false precision, and recognize

the systemic effects of all assessments and indicators.

In addition, indicators can be scrutinized regularly

and updated in the obtained ontological database.

Furthermore, the proposed ontology-based research

reports can be integrated into a single environment –

ontology repositories, as suggested before (Paschke

and Sch

afermeier, 2018).

The process starts with paper creation. For this

stage, we can use various text editors, for example,

word or google Docs. Then expert or author of the

paper will formulate metadata, which is necessary for

the ontology. For this purpose, the author will use

Microsoft Excel or Google Sheets. Then, an editor

needs to add information to the graph. In our case,

the IT Platform Polyhedron is used for this. And last

but not least, it is possible to use the “Alternative”

system, which includes Audit, Filtering, and Ranking

instruments. All proposed tools are illustrated in the

workﬂow diagram below.

It is worth mentioning that this methodology of

the centralized information web-oriented educational

environment of Ukraine has been developed, and with

ontological approach is more systematic now. Educa-

tional programs are essential to the world picture that

is given to people during education, so they contain

all basic terms that may be used to systemize other

ﬁelds of human activities, including scientiﬁc studies.

Like researchers, pupils interested in terms can also

use such speciﬁc term nodes to continue their study-

ing by investigating the studies conducted.

7 CONCLUSIONS

An ontological approach to scientiﬁc work systemati-

zation has been proposed, assuring compatibility. A

system for arranging research reports based on digital

taxonomies (ontologies) has been created. It allows

users to construct node hierarchies utilizing the natu-

ral structure of the reports. Concrete parameters were

added to the nodes as metadata (semantic, numeric,

images, and links) to enable Polyhedron tools pro-

cessing. Ranging and ﬁltering were employed to han-

dle semantic and numerical metadata. The obtained

results allow for interchange across various study re-

ports (including educational). The “Leiden Manifesto

of Scientometrics” is the acknowledged ontological

method.

Further study will improve interoperability across

research works by developing a single taxonomy that

provides hierarchization using the same methodolo-

gies, literature, and report ﬁndings and its process-

ing using both methods suggested in the research and

newly developed ones.

For the ﬁrst time, it presents the concept of in-

tegration of scientiﬁc studies ontologies with educa-

tional programs that make them more usable for both

students and young researchers. The proposed ap-

proach aligns with the centralized informational web-

oriented educational environment concept.

REFERENCES

Abramo, G., D’Angelo, C. A., and Di Costa, F.

(2011). University-industry research collaboration:

a model to assess university capability. Higher

Education, 62(2):163–181. https://doi.org/10.1007/

s10734-010-9372-0.

Alnemr, R., Paschke, A., and Meinel, C. (2010). Enabling

Reputation Interoperability through Semantic Tech-

nologies. In Proceedings of the 6th International Con-

ference on Semantic Systems, I-SEMANTICS ’10,

New York, NY, USA. Association for Computing Ma-

chinery. https://doi.org/10.1145/1839707.1839723.

Amami, M., Faiz, R., Stella, F., and Pasi, G. (2017). A

Graph Based Approach to Scientiﬁc Paper Recom-

mendation. In Proceedings of the International Con-

ference on Web Intelligence, WI ’17, page 777–782,

New York, NY, USA. Association for Computing Ma-

chinery. https://doi.org/10.1145/3106426.3106479.

Bilyk, Z. I., Shapovalov, Y. B., Shapovalov, V. B., Me-

galinska, A. P., Zhadan, S. O., Andruszkiewicz, F.,

Dołha

nczuk-

odka, A., and Antonenko, P. D. (2022).

Comparison of Google Lens recognition performance

with other plant recognition systems. Educational

Technology Quarterly, 2022(4):328–346. https://doi.

org/10.55056/etq.433.

Boughareb, D., Khobizi, A., Boughareb, R., Farah, N.,

and Seridi, H. (2020). A Graph-Based Tag Rec-

ommendation for Just Abstracted Scientiﬁc Articles

Tagging. International Journal of Cooperative Infor-

mation Systems, 29(03):2050004. https://doi.org/10.

1142/S0218843020500045.

Braun, T., Gl

anzel, W., and Schubert, A. (2001). Publica-

tion and cooperation patterns of the authors of neu-

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

141

roscience journals. Scientometrics, 51(3):499–510.

https://doi.org/10.1023/A:1019643002560.

Globa, L., Kovalskyi, M., and Stryzhak, O. (2015). In-

creasing Web Services Discovery Relevancy in the

Multi-ontological Environment. In Wili

nski, A., Fray,

I. E., and Peja

s, J., editors, Soft Computing in Com-

puter and Information Science, volume 342 of Ad-

vances in Intelligent Systems and Computing, pages

335–344, Cham. Springer International Publishing.

https://doi.org/10.1007/978-3-319-15147-2 28.

Globa, L. S., Sulima, S., Skulysh, M. A., Dovgyi, S., and

Stryzhak, O. (2019). Architecture and Operation Al-

gorithms of Mobile Core Network with Virtualization.

In Ortiz, J. H., editor, Mobile Computing, Rijeka. In-

techOpen. https://doi.org/10.5772/intechopen.89608.

Khasseh, A. A., Soheili, F., Moghaddam, H. S., and Chelak,

A. M. (2017). Intellectual structure of knowledge in

iMetrics: A co-word analysis. Information Process-

ing & Management, 53(3):705–720. https://doi.org/

10.1016/j.ipm.2017.02.001.

Kinouchi, O., Soares, L. D., and Cardoso, G. C. (2018).

A simple centrality index for scientiﬁc social recog-

nition. Physica A: Statistical Mechanics and its Ap-

plications, 491:632–640. https://doi.org/10.1016/j.

physa.2017.08.072.

Kostenko, L., Zhabin, A., Kuznetsov, A., Lukashevich, T.,

Kukharchuk, E., and Simonenko, T. (2015). Sciento-

metrics : A Tool for Monitoring and Support of Re-

search. Science and Science of Science, (3):88–94.

http://nbuv.gov.ua/UJRN/NNZ 2015 3 12.

Lawani, S. M. (1986). Some bibliometric correlates of qual-

ity in scientiﬁc research. Scientometrics, 9(1):13–25.

https://doi.org/10.1007/BF02016604.

Milojevi

c, S. and Leydesdorff, L. (2013). Information

metrics (iMetrics): a research specialty with a socio-

cognitive identity? Scientometrics, 95(1):141–157.

https://doi.org/10.1007/s11192-012-0861-z.

Modlo, Y. O., Semerikov, S. O., Nechypurenko, P. P., Bon-

darevskyi, S. L., Bondarevska, O. M., and Tolmachev,

S. T. (2019). The use of mobile Internet devices

in the formation of ICT component of bachelors in

electromechanics competency in modeling of techni-

cal objects. CTE Workshop Proceedings, 6:413–428.

https://doi.org/10.55056/cte.402.

Modlo, Y. O., Semerikov, S. O., and Shmeltzer, E. O.

(2018). Modernization of Professional Training of

Electromechanics Bachelors: ICT-based Competence

Approach. In Kiv, A. E. and Soloviev, V. N., edi-

tors, Proceedings of the 1st International Workshop on

Augmented Reality in Education, Kryvyi Rih, Ukraine,

October 2, 2018, volume 2257 of CEUR Workshop

Proceedings, pages 148–172. CEUR-WS.org. https:

//ceur-ws.org/Vol-2257/paper15.pdf.

Mulla, K. R. (2012). Identifying and mapping the infor-

mation science and scientometrics analysis studies in

India (2005-2009): A bibliometric study. Library

Philosophy and Practice, page 772. https://www.

researchgate.net/publication/215709587.

Newman, M. E. J. (2001). The structure of scientiﬁc col-

laboration networks. Proceedings of the National

Academy of Sciences, 98(2):404–409. https://doi.org/

10.1073/pnas.98.2.404.

Nicolescu, B., editor (2008). Transdisciplinarity: Theory

and Practice. Hampton Press, 1 edition.

Oriokot, L., Buwembo, W., Munabi, I. G., and Kijjambu,

S. C. (2011). The introduction, methods, results and

discussion (IMRAD) structure: a Survey of its use

in different authoring partnerships in a students’ jour-

nal. BMC Research Notes, 4(1):250. https://doi.org/

10.1186/1756-0500-4-250.

Paperpile (2019). What are the different types

of research papers? https://paperpile.com/g/

types-of-research-papers/.

Parveen, D. (2018). A Graph-based Approach for the

Summarization of Scientiﬁc Articles. PhD thesis,

Ruprecht-Karls-Universit

at Heidelberg. https://doi.

org/10.11588/heidok.00027924.

Paschke, A. and Sch

afermeier, R. (2018). OntoMaven

- Maven-Based Ontology Development and Man-

agement of Distributed Ontology Repositories. In

Nalepa, G. J. and Baumeister, J., editors, Synergies

Between Knowledge Engineering and Software Engi-

neering, volume 626 of Advances in Intelligent Sys-

tems and Computing, pages 251–273. Springer Inter-

national Publishing, Cham. https://doi.org/10.1007/

978-3-319-64161-4 12.

Pavlovskiy, I. S. (2017). Using Concepts of Scientiﬁc Ac-

tivity for Semantic Integration of Publications. Pro-

cedia Computer Science, 103:370–377. XII Interna-

tional Symposium Intelligent Systems 2016, INTELS

2016, 5-7 October 2016, Moscow, Russia. https://doi.

org/10.1016/j.procs.2017.01.123.

Perraudin, N. (2017). Graph-based structures in data sci-

ence: fundamental limits and applications to ma-

chine learning. PhD thesis,

Ecole Polytechnique

erale De Lausanne. https://infoscience.epﬂ.ch/

record/227982?ln=en.

Perron, B. E., Victor, B. G., Hodge, D. R., Salas-Wright,

C. P., Vaughn, M. G., and Taylor, R. J. (2017). Lay-

ing the foundations for scientometric research: A

data science approach. Research on Social Work

Practice, 27(7):802–812. https://doi.org/10.1177/

1049731515624966.

Pianta, M. and Archibugi, D. (1991). Specialization and

size of scientiﬁc activities: A bibliometric analysis of

advanced countries. Scientometrics, 22(3):341–358.

https://doi.org/10.1007/BF02019767.

Popova, M. and Stryzhak, O. (2013). Ontological interface

as a means of presenting information resources in the

GIS. Uchenye zapiski Tavricheskogo natcionalnogo

universiteta imeni V. I. Vernadskogo. Seriia “Ge-

ograﬁia”, 26(65)(1):127–135. https://tinyurl.com/

5fhtrmck.

Ramesh Babu, A. and Singh, Y. P. (1998). Determinants of

research productivity. Scientometrics, 43(3):309–329.

https://doi.org/10.1007/BF02457402.

Ram

ırez, M. C. and Rodr

ıguez Devesa, R. A. (2019). A sci-

entometric look at mathematics education from Sco-

pus database. The Mathematics Enthusiast, 16(1-

3):37–46. https://doi.org/10.54870/1551-3440.1449.

AET 2021 - Myroslav I. Zhaldak Symposium on Advances in Educational Technology

142

Ravikumar, S., Agrahari, A., and Singh, S. N. (2015). Map-

ping the intellectual structure of scientometrics: a co-

word analysis of the journal Scientometrics (2005–

2010). Scientometrics, 102(1):929–955. https://doi.

org/10.1007/s11192-014-1402-8.

Shapovalov, V. B., Shapovalov, Y. B., Bilyk, Z. I., Ata-

mas, A. I., Tarasenko, R. A., and Tron, V. V. (2019).

Centralized information web-oriented educational en-

vironment of ukraine. CTE Workshop Proceedings,

6:246–255. https://doi.org/10.55056/cte.383.

Shapovalov, Y. B., Shapovalov, V. B., Bilyk, Z. I., and

Shapovalova, I. M. (2022). Structurization of educa-

tional expedition studies in the form of taxonomies.

Educational Dimension, 7:130–149. https://doi.org/

10.31812/educdim.7618.

Slipukhina, I., Kuzmenkov, S., Kurilenko, N., Mieniailov,

S., and Sundenko, H. (2019). Virtual Educational

Physics Experiment as a Means of Formation of the

Scientiﬁc Worldview of the Pupils. In Ermolayev,

V., Mallet, F., Yakovyna, V., Mayr, H. C., and Spi-

vakovsky, A., editors, Proceedings of the 15th Inter-

national Conference on ICT in Education, Research

and Industrial Applications. Integration, Harmoniza-

tion and Knowledge Transfer. Volume I: Main Con-

ference, Kherson, Ukraine, June 12-15, 2019, volume

2387 of CEUR Workshop Proceedings, pages 318–

333. CEUR-WS.org.

Stryzhak, O., Horborukov, V., Prychodniuk, V., Franchuk,

O., and Chepkov, R. (2021). Decision-making System

Based on The Ontology of The Choice Problem. Jour-

nal of Physics: Conference Series, 1828(1):012007.

https://doi.org/10.1088/1742-6596/1828/1/012007.

Stryzhak, O. Y., Horborukov, V. V., Franchuk, O. V., and

Popova, M. A. (2014). Ontology selection prob-

lem and its application to the analysis of limnologi-

cal systems. Ecological safety and nature manage-

ment, 15:172–183. http://nbuv.gov.ua/UJRN/ebpk

2014 15 21.

Velychko, V., Popova, M., Prykhodniuk, V., and Stryzhak,

O. (2017). TODOS – IT-platform formation trans-

disciplinaryn information environment. Systems of

Arms and Military Equipment, (1(49)):10–19. http:

//nbuv.gov.ua/UJRN/soivt 2017 1 4.

Vlasenko, K., Chumak, O., Lovianova, I., Kovalenko, D.,

and Volkova, N. (2020). Methodical requirements

for training materials of on-line courses on the plat-

form “Higher school mathematics teacher”. E3S Web

of Conferences, 166:10011. https://doi.org/10.1051/

e3sconf/202016610011.

Volckmann, R. (2007). Transdisciplinarity: Basarab Nico-

lescu Talks with Russ Volckmann. Integral Review,

(4):73–90. https://tinyurl.com/39y6fdm3.

Yahupov, V. V., Kyva, V. Y., and Zaselskiy, V. I. (2020).

The methodology of development of information and

communication competence in teachers of the mili-

tary education system applying the distance form of

learning. CTE Workshop Proceedings, 7:71–81. https:

//doi.org/10.55056/cte.312.

Zhadan, S., Shapovalov, Y., Tarasenko, R., and Salyuk,

A. (2021). Development Of An Ammonia Pro-

duction Method For Carbon-Free Energy Genera-

tion. Eastern-European Journal of Enterprise Tech-

nologies, 5(8-113):66 – 75. https://doi.org/10.15587/

1729-4061.2021.243068.

The Taxonomies of Educational and Scientiﬁc Studies Role in Centralized Informational Web-Oriented Educational Environment

143