=Paper=
{{Paper
|id=Vol-2879/paper02
|storemode=property
|title=Comparison of ontology with non-ontology tools for educational research
|pdfUrl=https://ceur-ws.org/Vol-2879/paper02.pdf
|volume=Vol-2879
|authors=Roman A. Tarasenko,Viktor B. Shapovalov,Stanislav A. Usenko,Yevhenii B. Shapovalov,Iryna M. Savchenko,Yevhen Yu. Pashchenko,Adrian Paschke
}}
==Comparison of ontology with non-ontology tools for educational research==
https://ceur-ws.org/Vol-2879/paper02.pdf
Comparison of ontology with non-ontology tools for
educational research
Roman A. Tarasenko1 , Viktor B. Shapovalov1 , Stanislav A. Usenko1 ,
Yevhenii B. Shapovalov1 , Iryna M. Savchenko1 , Yevhen Yu. Pashchenko1 and
Adrian Paschke2
1
The National Center “Junior Academy of Sciences of Ukraine”, 38-44 Degtyarivska Str., Kyiv, 04119, Ukraine
2
Fraunhofer FOKUS (with support of BMBF “Qurator” 03WKDA1F), Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany
Abstract
Providing complex digital support for scientific research is an urgent problem that requires the creation of
useful tools. Cognitive IT-platform Polyhedron has used to collect both existing informational ontology-
based tools, and specially designed to complement a full-stack of instruments for digital support for
scientific research. Ontological tools have generated using the Polyhedron converter using data from
Google sheets. Tools “Search systems”, “Hypothesis test system”, “Centre for collective use”, “The
selection of methods”, “The selection of research equipment”, “Sources recommended by Ministry of
Education and Science of Ukraine”, “Scopus sources”, “The promising developments of The National
Academy of Sciences of Ukraine” were created and structured in the centralized ontology. A comparison
of each tool to existing classic web-based analogue provided and described.
Keywords
cognitive IT-platform Polyhedron, ontology, ontology tool, system, scientific method, scientific tool
1. Introduction
Nowadays, to increase the convenience and efficiency of data processing, the active digital
transformation of all of the areas of human activity [1, 2, 3, 4, 5, 6] is underway.
The scientific method is a way that researchers used for many years. However, until now,
there are no approaches that can support the research process in educational research. For
example, in Crus et al. [7] work, the research process considered as only three cyclical stages:
Composition, Execution, and Analysis. But in this article, the term “scientific method” has used
CTE 2020: 8th Workshop on Cloud Technologies in Education, December 18, 2020, Kryvyi Rih, Ukraine
" tarasenko@man.gov.ua (R. A. Tarasenko); farkry17@gmail.com (S. A. Usenko); sjb@man.gov.ua
(Y. B. Shapovalov); savchenko.man@gmail.com (I. M. Savchenko); pobeda2000@gmail.com (Y. Yu. Pashchenko);
paschke@inf.fu-berlin.de (A. Paschke)
~ http://www.nas.gov.ua/UA/PersonalSite/Pages/default.aspx?PersonID=0000029045 (V. B. Shapovalov);
http://www.nas.gov.ua/UA/PersonalSite/Pages/default.aspx?PersonID=0000026333 (Y. B. Shapovalov);
http://www.nas.gov.ua/UA/PersonalSite/Pages/default.aspx?PersonID=0000020650 (I. M. Savchenko);
http://www.nas.gov.ua/UA/PersonalSite/Pages/default.aspx?PersonID=0000010204 (Y. Yu. Pashchenko)
0000-0001-5834-5069 (R. A. Tarasenko); 0000-0001-6315-649X (V. B. Shapovalov); 0000-0002-0440-928X
(S. A. Usenko); 0000-0003-3732-9486 (Y. B. Shapovalov); 0000-0002-0273-9496 (I. M. Savchenko);
0000-0001-8703-4796 (Y. Yu. Pashchenko); 0000-0003-3156-9040 (A. Paschke)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
82
according to one of the most popular versions. The scientific method can be presented by the
set of stages [8] that are shown as a simple algorithmic scheme in figure 1.
Figure 1: Algorithmic scheme stages of the scientific method.
The scientific method is often used in the educational process. Quite often, teachers require
to research to complete an essay. There are various school competitions of scientific works
such as the competition of scientific articles of the Junior Academy of Sciences of Ukraine,
international competitions, those provided by international programs and other [9].
Often it is difficult for students and pupils to perform a scientific method and therefore,
to simplify it, several authors suggested the use of ontological systems [10, 7, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21]. But they did not use on all stages of scientific methods used during
educational researches.
It is possible to use a digital ontological-based approach to improve structuration, interactivity.
Smith [20] believed in the effect that the authoring and maintenance and evaluation of scientific
ontologies is an incremental, empirical, cumulative, and collaborative (i.e., precisely, scientific)
activity that must be carried out by experts in the relevant scientific domains. Ontologies has
used to sovling of practical oriented problems based on formalization of the contexts [22] and
for creation of repositories [23].
In this article, an “ontological tool” is a term, that means some software or web system, that
consists of nodes with specific data and provide solving of some problem during educational
research. The node from which all branches go is called the parent or root. The top from
which no ribs protrude is called a leaf. The other nodes are called child nodes. If there are
no additional branches in the graph from the parent node, then this ontology is called simple.
Also, a characteristic feature of ontologies systems is that multiple ontologies can be filled with
concepts of various levels of complexity [10].
Ontologies have been using to visualize the results of the already performed experiment.
83
In Crus et al. [7] work, an ontological system named “Open proVenance” was developed. The
root node in their ontology is the name of the experiment, from which withdraws names and
surnames of specific performers and their role in the study, and the leaf node is a specific
measured indicator, and its value (for example, pH 1). The system is based on the “Open
proVenance” Model and the Unified Foundational Ontology. This ontological system can be
useful only on the “Test with an Experiment” stage of the scientific method.
To create the structure of the all research process, an ontological system called “Elements
of a common ontology of scientific experiments” (EXPO) [7] had developed. The root node in
such application is the name of the research with its metadata (hypothesis, goal, conclusion
etc.), from which depart factors (the child nodes) that may affect the experiment and its result.
The leaf node is a specific scientific experiment, and its attributes indicate its name (a precision
measurement of the mass of the top quark). EXPO based on the W3C standard ontology language
OWL-DL. This ontological system can be useful only at the stage of “Test with an Experiment”
and “Analyse results and Conclude” of the scientific method.
Ontology constructor MoKi [11, 12] developed for creating a structured ontology from
Wikipedia articles and devoted to providing a literature review. The user can present the
creation of their ontologies based on the Wiki articles he needs during the literature review.
However, in any ontology created using Moki, there no root node, and all of them are looped.
Nodes in Moki are a Wiki article connected to the other child node (other wiki articles). Moki is
multiplatform and supports various ontological generators (Amine, Protégé, etc.).
It can be useful in the “Do Background Research” stage of educational researchers. At the
same time, it is limiting by the Wikipedia database.
There are also more specific ontological systems designed for the scientific method. For
example, an ontological database “Gene Ontology” [20] had developed and designed to obtain
detailed information about genes. The root node in such application is called the gene classifier
from which branch the filters (child nodes) used by geneticists (e.g. biological process, cellular
component, molecular function, and others). The leaf node is a specific gene with its name (e.g.
ABIN2-NFKB1-MAP3K8) and attributes which are keys semantic characteristics that describe
the gene (e.g. Definition, Gene products, Synonyms, Ontology ID space, and others). The system
based on the Open Biomedical Ontologies repository. It can be useful on the “Do Background
Research” and “Analyse results and Draw conclusion” stages, but can be helpful only for the
specialists in the genetic field.
All these ontological systems will be useful only at certain separate stages of scientific method
such as “Do background research”, “Construct Hypothesis” and “Report Results”, and in most
cases only for specialists in separate fields. So, none of the ontological systems previously
proposed couldn’t offer a universal and complex method to provide digital cloud-based support
of educational researches. Also, all these systems haven’t integrated. That means, all these
systems cannot fully interact with each other’s ontologies. Users must choose between them
or feel discomfort memorizing and switching between them. The results of the comparison of
ontological systems in the scientific methods have shown in table 1.
Besides, a common disadvantage of all considered systems [7, 10, 11, 13, 14, 20, 21, 12, 15, 16,
17, 18, 19] is unsuitability for use by pupils and novice researchers due to the complexity of
using. For example, “Open proVenance” requires using both nodes and classes, which requires
additional specific knowledge and additional time to create an ontology.
84
Table 1
The results of the comparison of scientific ontological systems
Name of ontol- Root node Leaf node Was built on Using on sci- Authors
ogy instrument entific method
stages
Open proVe- Name of the ex- Measured indi- Open Prove- Report the re- Sergio Manuel
nance Ontolo- periment cator its value nance Model sults of experi- Serra Crus,
gyt and the Unified ment and test Maria Luiza
Foundational the experiment Machado Cam-
Ontology pos and Marta
Mattoso
MoKi There no ini- Wiki article Multiplatform Report the re- Alessio Bosca,
tial node and in ontological and sup- sults and test Matteo Casu,
all of them are form ports various the experiment Mauro Drag-
looped ontological oni and Andi
generators Rexha
EXPO The name of A specific Based on the Test with on Ex- Larisa N. Solda-
the experiment scientific exper- W3C stan- periment and tova, Ross D.
with its meta- iment dard ontology Analise results King
data language OWL- of experiment
DL
Gene Ontology The gene classi- Gene with their Based on the Report the re- Barry Smith
fier name Open Biomedi- sults and test
cal Ontologies the experiment
repositorys
So, it seems relevant to provide digital support of educational researchers provided by the
scientific method using uncomplicated and understandable tools. Unlike observed systems,
tools developed using IT-platform Polyhedron are simple to use, and it is possible to create all
primary instruments in one environment.
This paper aims to develop the system of the most common ontology-based tools used by
pupils during educational researches using the scientific method characterized by advantages
compare to non-ontological-based tools. To provide it, IT-platform Polyhedron has used due to
its simplicity. These functions can provide semantic web, systematization, internal and external
searches [24] and transdisciplinary support.
This system is multi-agent, and internal sources can be used as agents. In such a way, IT-
platform Polyhedron allows to provide of transdisciplinary and interactivity of educational
research[24, 25]. In the environment of the Polyhedron platform, the construction of all chains
of the process transdisciplinary integrated interaction is ensured [26].
Besides, cognitive IT-platform Polyhedron has all advantages of the ontological information
representation [27, 28, 29]. The ontological interface has provided by the procedure of activation
of multiple binary taxonomy relationships. It is an intelligent means of user interaction with an
ontology-based information system [26].
The cognitive IT platform Polyhedron platform can provide the digitalization of the scientific
85
method in the learning process. Also, this system can be useful for the education process in
general by creating a centralized Information web-oriented educational environment [24, 30].
All proposed instruments can be used together with different modern educational and scientific
methods like an augmented reality [31, 32, 33, 34], and distance learning [35, 36].
2. Materials and methods
For creating digital instruments, the sheets with data have loaded to editor4, the part of the
cognitive IT platform Polyhedron. After that, the generation of the graph nodes with its
characteristics have carried out. To provide information storage and exchange Google sheets
were used to store data, with their further conversion into the .xls and .csv Excel sheets (see
figure 2).
Figure 2: Google sheet with data.
The obtained documents have used to create the ontological structure .xml and to fill the
ontology graphs with semantic and numeric information for ranking or filtering. Some of the
instruments to the web-oriented educational environment is using artificial intellectual features
of the cognitive IT platform Polyhedron to provide additional semantic characteristics.
The received documents have used to create an ontology structure (.xls) and to fill the ontology
graphs of ranking and filtering. To provide it, they were downloaded in editor4, the part of
the cognitive IT platform Polyhedron. After that, the graph generation and the inputting of
semantic characteristics to each vertex have carried out. Ontological edges have formed using
predicate equations which described in previous work [26].
For the development of some ontological tools, specific “audit” and “ranking” [37, 38, 39]
instruments have used. Both of them based on “Alternative” module, which has described in
previous works [40]. To use “Alternative” a module has been created nodes of the graph with
semantic data grouped in semantic classes that will be ranking criteria. IT platform Polyhedron
is an innovative complex of programmatic information and methodological knowledge man-
agement tools, which is using ontological management approaches to corporate information
resources. Users are considered as the source for new knowledge, for transferring it in the form
86
of their knowledge through the tool IT platform Polyhedron, which is the only integrated point
of access – “the single window” – to the information and applications of the system to provide
interactive interaction with users. A key benefit of this system is the context-based method of
data processing and structuring based on semantic relations.
IT Platform Polyhedron allows users creating a system or graph, read, update a system or
graph, delete a system or graph and update the system configurations or graph configurations.
All these sections we can split into several different subsections that are named: customization,
data creation, information searching, data processing, data structuration, data validation, data
isolation, data visualization and data deletion. Every different user has a different role in
IT Platform Polyhedron. The Expert can create graphs, delete graphs, add metadata, edit
metadata. Thus, the Expert is responsible for creating term fields and filling them with data for
further processing in the Polyhedron IT platform. The IT Platform Polyhedron administrator
performs specialized functions – the formation of a public library of ontologies and the system
administration of transdisciplinary representation. The Young researcher can only read the
necessary data, for individual purpose. UML different types of users functions diagram is shown
in figure 3.
2.1. Criterion of the searching systems comprising
Search systems and scientometrics bases have compared with each other and the cognitive IT
platform Polyhedron search system according to the following criteria: “Content integration”,
“Lack of advertising”, “Interoperability with scientific and a patent search”, “Data security” and
“Data Availability”, “Indexing of educational programs”.
Search systems which in response to the user’s query, provides all types of data (links,
graphical results, semantic characteristics) meet the criterion of “Content integration”, and
those of them. Search systems which characterized by the lack of advertising met the criterion of
“Lack of advertising”. Search systems that provide results in the form of articles and patents have
considered to meet the criterion “Interoperability with scientific and a patent search”. Search
systems which do not find any malicious programs and viruses, meet the criterion of “Data
security”. Search systems which don‘t have no one restrictions on access to information (for
example, the fee for access or a mandatory registration) meet the criterion of “Data Availability”.
Search systems that can use data directly from educational programs and integrated with them
has been evaluated as meet the criterion “Indexing of educational programs”.
2.2. Criterion of the research tool systems comprising
Proposed ontology-information solutions have compared with their web-oriented analogue
criteria (except search systems) according to the following criteria: “Customization potential”,
“Multifunctionality of information processing”, “Data structuration”, “Availability of adaptive
interface”, “Data validation”, “Multi-user support”, “Data isolation”.
“Customization potential” criterion has used to evaluate possibility of the simply interaction
with the system to provide adaptive analysis. Criterion “Multifunctionality of information
processing” to evaluate possibility of the systems to provide data processing using few algorithms
in same time. If all information is structured, easy to read, and perceived by the user the systems
87
Figure 3: UML different types of user’s functions diagram.
has been evaluated as meet criterion “Data structuration”. The criterion “Availability of adaptive
interface” means that the system will be convenient in use for any circle of users, regardless of
their computer literacy level. “Data validation” criterion has used to evaluate functionality of
data validation by experts (on the absence of inaccurate or incorrect information on the resource
and its coresponding to the actucal stadarts; for example, educational programs and national
standard such as on the names of chemical compounds used during educational process (DSTU
2439:2018). The criterion “Multi-user support” indicates that the document in the system can be
changed at one time by multiple users. “Data isolation” criterion means that system can provide
access rights to information according to user roles and publish in the search only those results
that relate to the user and his interests. “Multi-user support” criterion has used to evaluate the
88
possibility of the systems to provide access management to information changing according to
user roles and publish in the search only those results that relate to the user and his interests.
3. Scientific method with using ontological tools
3.1. The general concept of ontological-based model based on Polyhedron
An ontology-based solution has developed to simplify the process of educational researches
using the scientific method. Such ontological solutions were: “Search systems ranking”, “Search
systems”, “Hypothesis test system”, “Centre for collective use”, “The selection of methods”, “The
selection of research equipment”, “Sources recommended by the Ministry of Education and
Science of Ukraine”, “Scopus sources”, “The promising developments of National Academy of
Sciences of Ukraine”.
For systematization, simplification, and providing of a single ecosystem, these tools have
compiled into the single simple ontology named “Scientific method”. It is structured according
to the stages of the scientific method as “Do Background Research”, “Construct Hypothesis”,
“Test with an Experiment”, “Analise results and Draw conclusion”, “Report Results” (see figure 4).
The “Ask questions” stage skipped because no software required at this stage. Each of the nodes
contains links to ontological tools, that can be used at an appropriate stage. The next part of
the article will devote to the analysis of these tools.
Figure 4: The general view of the ontology-based model.
3.2. Stage “Do background research” of educational researches
Tools like search sites (Google, Bing, Yahoo, etc.) and scientometric databases (Scopus, Web
of Science, CiteseerX, Microsoft academic, a miner, refseek, BASE (Bielefeld Academic Search
Engine), WorldWideSciense, JURN, Google scholar, and Google patent and others) have rep-
resented in the “Do Background Research” ontological node. Each child node were a specific
search system or a scientometrics database with a link to it. The general view of “Search systems”
ontology has presented in figure 5.
89
Figure 5: General view of the “Search systems” ontology.
The advantage of the cognitive IT platform Polyhedron internal search function is using
an algorithm, which conducted between the ontological graph with nodes. Additionally, this
algorithm can provide isolation and validation of information based on experts’ decisions called
internal search. That led to extended security and an increase in searching for material quality.
This is significantly important in conditions of developing science society, that led to dynamic
changes of the standards, as was with names of chemical substances of substance in Ukraine
last year. The proposed in this article system also has its search engine (internal and external)
described in previous works [24].
Scopus, Web of Science, CiteseerX, Microsoft academic, aminer, refseek, BASE (Bielefeld Aca-
demic Search Engine), WorldWideSciense, JURN, Google Scholar, Google patent have evaluated
as particularly meet the criterion “The content integration”. Scopus, Web of Science, CiteseerX
have assessed partly, because they provide only necessary information about article and their
metadata.
Scopus, Web of Science, CiteseerX, Microsoft academic, aminer, refseek, BASE (Bielefeld Aca-
demic Search Engine), WorldWideSciense, JURN, Google Scholar, Google patent have evaluated
as partly meet the criterion “Interoperability with scientific and a patent search”, because they
provide search only among between scientific publications or patents in the one time. Google
has evaluated as partly meet the criterion “Interoperability with scientific and a patent search”
partly because it publishes results of search not only in the form of scientific publications and
patents.
Scopus, Web of Science, CiteseerX, Microsoft academic, aminer, refseek, BASE (Bielefeld Aca-
demic Search Engine), WorldWideSciense, JURN, Google Scholar, Google patent have evaluated
as partly meet the criterion “Data security” and “Data Availability”, because some search results
require a fee for full access to information or mandatory registration on the website.
Google has evaluated as partly meet the criterion “Indexing of educational programs”, because
it publishes search results primarily in the form of links on normative documents containing
educational programs. The search systems have compared to each other. The results of the
comparison are shown in table 2.
Thus, the comparison has found that the Polyhedron search system is more appropriate to
use because it fully meets all the criteria. Also, has been found and confirmed that the Google
search is more suitable for daily search and external literature review, as it meets such criterion:
90
Table 2
The result of the comparison search system
Search system and Content in- Lack of ad- Interoperability Data se- Data avail- Indexing of
scientometrics bases tegration vertising with scientific curity ability educational
name and a patent programs
search
Polyhedron search Yes Yes Yes Yes Yes Yes
system
Scopus Partly Yes Partly Partly Partly No
Web of Science Partly Yes Partly Partly Partly No
CiteseerX Partly Yes Partly Partly Partly No
Microsoft academic Partly Yes Partly Partly Partly No
aminer Partly Yes Partly Partly Partly No
refseek Partly Yes Partly Partly Partly No
BASE (Bielefeld Aca- Partly Yes Partly Partly Partly No
demic Search Engine)
WorldWideSciense Partly Yes Partly Partly Partly No
JURN Partly Yes Partly Partly Partly No
Google Scholar Partly Yes Partly Partly Partly No
Google patent Partly Yes Partly Partly Partly No
Google Yes No Partly No No Partly
“Content integration”, but do not meet criteria: “Lack of advertising”, “Data security” and “ Data
availability”, and only partly meet criteria: “Interoperability with scientific and a patent search”,
“Indexing of educational programs”. The rest of the considered systems are suitable only for
in-depth scientific research because they meet the criterion “Lack of advertising” and partly
meet by the following criteria “Content integration”, “Interoperability with scientific and a
patent search”, “Data security” and “Data availability”.
Therefore, the usage of ranking system can be more relevant, comparing to existing ap-
proaches (searching systems). The ranking system expect preparation of numeric data from
scientific papers(reports). It is possible due to the experimental papers includes the same infor-
mation, for example, different works in the field of anaerobic digestion. All research papers
about anaerobic digestion include data processing parameters such as temperature, type of
substrate, reactor volume, moisture content, initial pH, parameters, characterises of the effi-
ciency of the process, biogas yield, methane content, average pH during the process, destruction
process etc [41]. An example of the ranking system on numeric data analysis of educational
researches is shown in figure 6.
The proposed approach involves the use of an ontology for the management of specialized
literature using other functions of the Polyhedron platform such as filtering (according to the
parameters created by the user), ranking, and audit (if the user needs it).
91
Figure 6: An example of input (a) and result interfaces of the ranking system on numeric data analysis
of educational researches
3.3. Stage of “Constructing Hypotheses” with using ontological tools
There is only one ontological tool “Hypothesis test system” for testing of hypotheses status only
this tool has represented in the “Construct Hypothesis” node. The Polyhedron platform has an
instrument to compare the hypotheses of several works. The instrument is a simple ontology,
where already have tested predictions from the scientific researches are semantic characteristics
of each node. Next, the audit function of the Polyhedron platform described in previous works
[37, 38, 39] find the affinity of the semantics and highlight by red colour those of hypothesis
which already tested. An example of the results of such an audit is presented in figure 7.
3.4. Stage of “Planning and test with an experiment” with using ontological
tools
At the stage of “Test with an Experiment” specific ontological tools have developed and rep-
resented in the general ontology as: “Centre for collective use”, “The selection of methods”,
“The selection of research equipment”. In Ukraine, it is possible to provide an experiment using
tools located in centres of collective use of the National Academy of Science. To simplify the
process of selecting the equipment, the web-based tool “Centre for collective use” has been
created. However, to simplify the interface and make it more useful, ontology with the same
data but with extended functionality have created. The leaf nodes of this ontology are analysis
devices. Visual comparison of ontological and non-ontological tools “Centre for collective use”
is presented in figure 8.
Non-ontology system “Centre for collective use” has several shortcomings, both visual and
functional which are obsolescence and inconvenience of the interface, inconvenient navigation
in the system, and the complete absence of a filtering system. These factors make the application
unsuitable for the selection of equipment during the process of planning of the experiment.
The proposed ontological-based tool “Centre for collective use” is having not only an up-
to-date interface but also several advantages. One of the key features is a stable semantic link
and the ability of the system to combine all of the innovative applications of digitalization of
the educational and research process. Also, have created an ontological-based system “Centres
92
Figure 7: General view of the audit results in the “Hypothesis test system” ontology.
Figure 8: General view systems for the selection of equipment in centre of collective usage during
planning the experiment in the non-ontology-based (a) and (b) ontological-based system.
of collective use” is conveniently classifying scientific equipment by departments of science it
belongs to. This feature was realized as non-user-friendly in traditional web-based tool.
Besides, the “Centre for collective use” use in the cognitive IT platform Polyhedron platform
has several useful filters, unlike web-based tool. These filters are “the sphere of science”, “section
of National Academy of Science of Ukraine institution belongs to”, “Location”, “object of study”,
and “measured parameter”. All these filters will be especially useful for novice researchers.
93
These two systems have compared each other. The result of the comparison is shown in table 3.
Table 3
The result of the comparison of two ontological-based with non-ontology-based systems for the selection
of equipment in “Centre of collective usage”.
Criterion name Non-ontology “Cen- Ontological-based system
tre for collective use” “Centre for collective use”
Customization potential No Yes
Multifunctionality of information processing No Yes
Data structuration No Yes
Availability of adaptive interface No Yes
Data validation Yes Yes
Multi-user support No Yes
Data isolation No Yes
As a result of the comparison, it has found that the “Centre for collective use” in the cognitive
IT platform Polyhedron is more appropriate to use, because it fully meets all the criteria.
It has established that the non-ontology-based version of the “Centre for collective use” is
undesirable for use because it doesn’t meet the following criteria: “Customization potential”,
“Multifunctionality of information processing”, “Data structuration”, “Availability of adaptive
interface”.
There are many potential cases of using “Centre for collective use” in the cognitive IT platform
Polyhedron. For example, the user needs to find a device that is located in Kyiv, and which
investigates atomic particles. As a result of the user request, the device is the Isochronous
cyclotron U-240 of the Institute of Nuclear Physics, which is located on Nauki Avenue. This
and some other examples of applications are shown in table 4.
Table 4
The list of examples of using the proposed filtering system
Filters Results
Case 1 Location: Kyiv The object of study: Atomic particles cyclotron U-240
Case 1 Location: Kyiv Purpose: Analysis of X-ray spectra Module for CEM INCAPen-
teFETx3
Case 3 Location: Lviv Purpose: Microscopic examinations Scanning electron micro-
scope EVO 40XVP
In the laboratory MANLab of the National Center of Junior Academy of Science centre of
collective usage of the research equipment devoted to the research education has been created.
The same approach to simplify (using the ontology) the selection of the equipment called
“Selection of equipment in MANLab” has been developed. Leaf node in this ontology is separate
equipment located in MANLab. The filters such as the parameter which needs definition,
“Measurement accuracy”, “Measuring range” “The parameter which needs definition” will be
useful for selection. The General view of filtering input system for “The selection of research
equipment in MANLab” ontology is shown in figure 9
94
Figure 9: General view of filtering input system for “The selection of research equipment in MANLab”
ontology.
Novice researchers can easily find the equipment in both, Centers of collective usage in
National Academy of Science and Junior Academy of Science. For example, the researcher needs
to provide the information about the content of heavy metals in the water, and it is already
known that the content is high. The system can provide both ranking and filtering for solving
the tasks. Any of these instruments will propose to use for this task the Universal polarograph
EKOTEST-VA. By Choosing of this instrument, novice researchers will be able to use the links
on manlab.inohst.com.ua web-page with detailed information on the equipment. The list of
cases of application the proposed filtering system is presented in table 5. The General view of
filtering input system for “The selection of methods” ontology is shown in figure 10.
The other routine tasks that need to be solved during the planing of the experiment (“Test
with an experiment stage) is choosing the methods of research. The main problems in that field
that a wide variety of methods are presented in the form of printed text (books or methodical
instructions) which is hard to process. However, using filtering systems of IT-platform Polyhe-
dron, it is possible to provide management and simplify this task. The ontology used to solve
this task called “The selection of methods”, and the leaf node of it is method itself with the
metadata. For example, the youth researcher “is required to select to determine the content of
Al (III) in water. As a result of a user request, the system will propose the photometric analysis
95
Table 5
The list of cases of application the proposed filtering system
Filters Results
Case 1 The parameter which needs definition: concentration of heavy Universal polaro-
metals in the liquid Measuring range: 0.1 𝜇g / dm3-1g / dm3 graph EKOTEST-VA
Case 1 The parameter which needs definition: CO2 concentration Mea- Carbon dioxide sen-
surement accuracy: 20% Measuring range: 350-5000% sor DT040
Case 3 The parameter which needs definition: O2 concentration Mea-
suring range: 0-12,5 mg / dm3 Oxygen sensor DT222A
Figure 10: General view of filtering input system for “The selection of methods” ontology.
methodology, and display all necessary information, including chemical utensils, reagents,
needed equipment. Finally, the result will contain a link to the web-methodology at Junior
Academy of Science web-environments (manlab.science or stemua.science [42, 43]) where it
will be detailly described and visualized by video demonstrations. The list of examples of using
the proposed filtering system is presented in table 6.
3.5. Stage of “Analise results and draw a conclusion” with using ontological
tools
An example of the application of the proposed ontological system has given at “Analise results
and Conclude” node. At the stage of result analysis, both offline tools like MS Office, PTS Mathcad
Origin Pro, and cloud-based like G Suite and Office 365. The Polyhedron IT-platform can allow to
process and present the results of researches. Semantic and numerical characteristics from Excel
or Google Sheets have used to construct graphs and diagrams. Also, the necessary data can be
taken from the existing ontological graph. This is followed by the standard method of creating
96
Table 6
The list of examples of using the proposed filtering system
Filters Results
Case 1 Purpose: Water quality analysis The parameter Investigation of water samples for alu-
which needs definition: Al (III) minum content by photometric method
Case 1 Type of analysis: qualitative The parameter which Deposition of proteins by mineral acids
needs definition: The presence of proteins
Case 3 Type of analysis: Titrimetric determination The Determination of vitamin C content in
parameter which needs definition: vitamin C food by iodometric method
for ontological graphs and further use the module to build graphs and charts. For demonstration
results of statistical researches on mortality from various diseases in Ukraine from 2016 to 2020
(include from COVID-19) were taken. The graph with these results of statistical surveys in the
Polyhedron system is presented in figure 11.
Figure 11: The graph with results of statistical surveys of on mortality from various diseases in Ukraine
from 2016 to 2020 by cognitive IT platform Polyhedron.
3.6. Stage of “Report results” with using ontological tools
The “Sources recommended by the Ministry of Education and Science of Ukraine”, “Scopus
sources” “The promising developments of National Academy of Sciences of Ukraine” ontological
systems have represented in “Report Results” node. Those instruments have compared with
their non-ontological web-analogues.
After providing the research and analysing of the results, it may seem relevant to publish
the data. Now in Ukraine, it is possible to can be divided into between the journals recom-
mended by the Ministry of Education and Science of Ukraine, and the journals indexed by
97
scientometric bases. However, choosing of the journals, is always the challenge, especially
for novice researchers and to simplify the tasks both ontological and non-ontological tools
is existing nowadays. The ontological tool developed using IT-platform Polyhedron consist
from the leaf nodes (separate journals) with semantic data. To simplify the tasks, the filters like
“Field of Science”, “Free of Charge Journals”, “Publication Languages” have developed. There
are web-oriented and ontological systems for the selection of sources recommended by the
Ministry of Education and Science of Ukraine. A general view of references recommended by
the Ministry of Education and Science of Ukraine in bought forms are shown in figure 12.
Figure 12: General view of sources recommended by view of sources recommended by Ministry of
Education and Science of Ukraine in (a) non-ontology-based (b) ontological-based form.
“Sources recommended by the Ministry of Education and Science of Ukraine” and “Scopus
sources” ontologies have created. Both of ontologies are complex and contains branching by
branches, of science, type, indexes, and other parameters of journals for publication. The final
child nodes are each journal for publication. Such necessary filters as language of the journal,
cost of publication (including frees) is absent in web-based application, which may limit it using.
For example, today researchers are increasingly paying attention to the citation style of the
journal. General view of Scopus sources in standard web-oriented and ontological form are
shown in figure 13. All these systems have compared to each other. The result of the comparison
is shown in table 7.
Figure 13: General view of Scopus sources in (a) non-ontology-based (b) ontological-based form.
98
Table 7
The result of the comparison of edition structuration systems
Criterion name “Sources rec- “Scopus “Sources recommended “Scopus
ommended by sources” by the Ministry of Ed- sources” by
the Ministry ucation and Science of cognitive
of Education Ukraine” in cognitive IT IT platform
and Science of platform Polyhedron Polyhedron
Ukraine”
Customization potential No No Yes Yes
Multifunctionality of in- No No Yes Yes
formation processing
Data structuration No No Yes Yes
Availability of adaptive No Yes Yes Yes
interface
Data validation Yes Yes Yes Yes
Multi-user support No No Yes Yes
Data isolation Yes Yes Yes Yes
As a result of the comparison, it has found that “Sources recommended by the Ministry of
Education and Science of Ukraine” in cognitive IT platform Polyhedron and “Scopus sources”
by cognitive IT platform Polyhedron are more appropriate to use because it fully meets all
the criteria. “Sources recommended by the Ministry of Education and Science of Ukraine” is
undesirable for use because it doesn’t meet the following criterion “Customization potential”,
“Multifunctionality of information processing”, “Data structuration”, “Availability of adaptive
interface”, “Multi-user support”. As a result of the comparison, it has established that the “Scopus
sources” is undesirable for use because it doesn’t meet the following criterion “Customization
potential”, “Multifunctionality of information processing”, “Data structuration”, “Multi-user
support”. So, ontology-based tools “Sources recommended by the Ministry of Education and
Science of Ukraine” and “Scopus sources” is more appropriate to use.
For presentation of research results was created “The promising developments in The Na-
tional Academy of Sciences of Ukraine” in web-oriented non-ontology form containing all the
promising scientific projects of Ukraine. Ontology-based tool” The promising developments
of National Academy of Sciences of Ukraine” ranking ontology has created with functions of
ranking and provides better information management. The ontology is simple with scientific
developments, as leaf nodes. General view all the promising projects of National Academy of
Sciences of Ukraine and result of the ranking ontology tool presented in figure 14.
This tool will be useful for potential investors who are looking for investments. For example,
investor requesting to find the most finalized developments “The promising developments in
The National Academy Sciences of Ukraine” by the cognitive IT platform Polyhedron, the system
will display the projects “contact digital thermography”, “fibre-optic thermometric system”,
“growing of structurally perfect diamond single crystals”, “Technology of support, and anchor
fastening of earthworks appointment” are the most finalized developments. The non-ontology
tool “The promising developments in The National Academy of Sciences of Ukraine” has been
compared with ontological-based form “The promising developments in The National Academy
99
Figure 14: General view of “The promising developments in The National Academy of Sciences of
Ukraine” (a) and result of the ranking ontology tool (b).
Sciences of Ukraine” by cognitive IT platform Polyhedron. The result of the comparison is
shown in table 8.
Table 8
The result of the comparison of “The promising developments of The National Academy of Sciences of
Ukraine” systems
Criterion name Non-ontology “The promising “The promising developments in
developments in The National The National Academy Sciences of
Academy of Sciences of Ukraine” Ukraine” by cognitive IT platform
Polyhedron
Customization potential No No
Multifunctionality of infor- No Yes
mation processing
Data structuration No Yes
Availability of adaptive inter- No Yes
face
Data validation Yes Yes
Multi-user support No Yes
Data isolation Yes Yes
4. Discussion
As a result of the comparison, it has been found that Ontological tools for the support of the
scientific method created by cognitive IT-platform Polyhedron are more appropriate to use
because they fully meet all of the comparison criteria. And all of the non-ontological tools for
the support of the scientific method only meet the criteria: “Availability of adaptive interface”,
“Data validation”, “Data isolation”. The overall result of the comparison is shown in table 9.
We can use the “search system” ontology in the background research stage, “Hypothesis test
100
Table 9
The overall result of the comparison of ontological and non-ontological tools
Criterion name Ontological tools Non-ontological tools
Customization potential Yes No
Multifunctionality of information processing Yes No
Data structuration Yes No
Availability of adaptive interface Yes Yes
Data validation Yes Yes
Multi-user support Yes No
Data isolation Yes Yes
system” can be used in the construct of hypothesis stage. Depending on the presence or absence
of the experiment, we can use two different ontological solutions “The selection of research
equipment” and “The selection of methods”. In the report results stage it is possible to use three
different ontologies “Scopus edition”, “The edition recommended by Ministry of education and
science of Ukraine” and “The promising developments of NASU”. All proposed ontological tools
are extensions and support the method as illustrated in the workflow diagram (see figure 15).
Figure 15: Workflow diagram of proposed ontological tools
5. Conclusions
A centralized ontological tool based on the IT platform Polyhedron consisting of “Search systems
ranking”, “Search systems”, “Hypothesis test system”, “Centre for collective use”, “The selection
of methods”, “The selection of research equipment”, “Sources recommended by the Ministry
of Education and Science of Ukraine”, “Scopus sources”, “The promising developments of The
National Academy of Sciences of Ukraine” has been created. These ontological tools can be used
during almost all stages of the scientific method used in educational research. As a result of the
comparison, it was found that all systems created by the cognitive IT-platform Polyhedron are
more appropriate to use because they fully meet all the comparison criteria.
References
[1] K. Kutzner, T. Schoormann, R. Knackstedt, Digital transformation in information systems
research: A taxonomy-based approach to structure the field, 26th European Conference
on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS
2018 (2018) 1–18.
101
[2] D. Trcek, Driving digital transformation through e-government, CEUR Workshop Pro-
ceedings 2422 (2019) 263–273.
[3] O. Pinchuk, O. Sokolyuk, O. Burov, M. Shyshkina, Digital transformation of learning
environment: Aspect of cognitive activity of students, CEUR Workshop Proceedings 2433
(2019) 90–101.
[4] M. P. Leshchenko, A. M. Kolomiiets, A. V. Iatsyshyn, V. V. Kovalenko, A. V. Dakal, O. O.
Radchenko, Development of informational and research competence of postgraduate and
doctoral students in conditions of digital transformation of science and education, Journal
of Physics: Conference Series 1840 (2021) 012057. URL: https://doi.org/10.1088/1742-6596/
1840/1/012057. doi:10.1088/1742-6596/1840/1/012057.
[5] A. V. Pikilnyak, N. M. Stetsenko, V. P. Stetsenko, T. V. Bondarenko, H. V. Tkachuk, Com-
parative analysis of online dictionaries in the context of the digital transformation of
education, CEUR Workshop Proceedings (2020, in press).
[6] N. V. Morze, O. V. Strutynska, Digital transformation in society: key aspects for model
development, Journal of Physics: Conference Series (2021).
[7] S. M. S. Crus, M. L. M. Campos, M. Mattoso, A foundational ontology to support scientific
experiments, CEUR Workshop Proceedings 938 (2012) 144–155.
[8] S. Buddies, Science Buddies, 2019. URL: https:/www.sciencebuddies.org/.
[9] A. Titin-Snaider, S. Griebel, A. Nistor, À. Gras-Velázquez, Education policies in Europe.
Scientix observatory report, Technical Report, 2018.
[10] L. N. Soldatova, R. D. King, An ontology of scientific experiments, Journal of the Royal
Society Interface 3 (2006) 795–803. doi:10.1098/rsif.2006.0134.
[11] S. Woolgar, J. Lezaun, The wrong bin bag: A turn to ontology in science and technology
studies?, Social Studies of Science 43 (2013) 321–340. doi:10.1177/0306312713488820.
[12] R. Palma, O. Corcho, A. Gómez-Pérez, P. Haase, A holistic approach to collaborative
ontology development based on change management, Journal of Web Semantics 9 (2011)
299–314. URL: https://linkinghub.elsevier.com/retrieve/pii/S1570826811000503. doi:10.
1016/j.websem.2011.06.007.
[13] A. Bosca, M. Casu, M. Dragoni, A. Rexha, Modeling, managing, exposing, and linking
ontologies with a wiki-based tool, Proceedings of the 9th International Conference on
Language Resources and Evaluation, LREC 2014 (2014) 1668–1675.
[14] P. Y. Vandenbussche, G. A. Atemezing, M. Poveda-Villalón, B. Vatant, Linked Open
Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web, Semantic
Web 8 (2017) 437–452. doi:10.3233/SW-160213.
[15] M. Poveda-Villalón, A. Gómez-Pérez, M. C. Suárez-Figueroa, OOPS! (OntOlogy Pitfall Scan-
ner!): An On-line Tool for Ontology Evaluation, International Journal on Semantic Web
and Information Systems 10 (2014) 7–34. URL: http://services.igi-global.com/resolvedoi/
resolve.aspx?doi=10.4018/ijswis.2014040102. doi:10.4018/ijswis.2014040102.
[16] A. Alobaid, D. Garijo, M. Poveda-Villalón, I. Santana-Perez, A. Fernández-Izquierdo,
O. Corcho, Automating ontology engineering support activities with OnToology,
Journal of Web Semantics 57 (2019). URL: https://doi.org/10.1016/j.websem.2018.09.003.
doi:10.1016/j.websem.2018.09.003.
[17] U. Yadav, G. S. Narula, N. Duhan, V. Jain, B. K. Murthy, Development and Visualization
of Domain Specific Ontology using Protege, Indian Journal of Science and Technology 9
102
(2016). doi:10.17485/ijst/2016/v9i16/88524.
[18] R. Poli, M. Healy, A. Kameas (Eds.), Theory and Applications of Ontology: Computer Ap-
plications, Springer Netherlands, Dordrecht, 2010. doi:10.1007/978-90-481-8847-5.
[19] J. Dupré, A process ontology for biology, Physiology News (2015) 33–34. URL: https:
//www.physoc.org/magazine-articles/a-process-ontology-for-biology/. doi:10.36866/
pn.100.33.
[20] B. Smith, Ontology (Science), Nature Precedings (2008). URL: http://www.nature.com/
articles/npre.2008.2027.1. doi:10.1038/npre.2008.2027.1.
[21] C. Ghidini, M. Rospocher, L. Serafini, Conceptual Modeling in Wikis: A Reference Ar-
chitecture and a Tool, Proceedings of the 4th International Conference on Information,
Process, and Knowledge Management (eKNOW 2012) (2012) 128–135. URL: http://www.
thinkmind.org/index.php?view=article{&}articleid=eknow{_}2012{_}6{_}10{_}60015.
[22] R. Schäfermeier, A. Paschke, H. Herre, Ontology design patterns for representing context
in ontologies using aspect orientation, CEUR Workshop Proceedings 2459 (2019) 32–46.
[23] A. Paschke, R. Schäfermeier, OntoMaven - Maven-based ontology development and man-
agement of distributed ontology repositories, Advances in Intelligent Systems and Comput-
ing 626 (2018) 251–273. doi:10.1007/978-3-319-64161-4_12. arXiv:1309.7341.
[24] V. Shapovalov, Y. Shapovalov, Z. Bilyk, A. Atamas, R. Tarasenko, V. Tron, Centralized infor-
mation web-oriented educational environment of Ukraine, CEUR Workshop Proceedings
2433 (2019) 246–255.
[25] Y. B. Shapovalov, V. B. Shapovalov, V. I. Zaselskiy, TODOS as digital science-support
environment to provide STEM-education, Proceedings of the 6th Workshop on Cloud
Technologies in Education (CTE 2018) 2433 (2019) 232–245. URL: http://ceur-ws.org/
Vol-2433/paper14.pdf.
[26] V. Velichko, M. Popova, Prikhodnyuk V., O. Y. Stryzhak, TODOS is an IT platform for the
formation of transdisciplinary information environments, Weapons systems and military
equipment 1 (2017) 10–19.
[27] M. Popova, O. Y. Stryzhak, Ontological interface as a means of presenting information
resources in the GIS environment, Scientific notes of the Taurida National University. V. I.
Vernadsky. 65 (2013) 127–135.
[28] M. Gruber, S. Eichstadt, J. Neumann, A. Paschke, Semantic Information in Sensor Net-
works: How to Combine Existing Ontologies, Vocabularies and Data Schemes to Fit a
Metrology Use Case, 2020 IEEE International Workshop on Metrology for Industry 4.0 and
IoT, MetroInd 4.0 and IoT 2020 - Proceedings (2020) 469–473. doi:10.1109/MetroInd4.
0IoT48571.2020.9138282.
[29] J. A. Qundus, S. Peikert, A. Paschke, AI supported Topic Modeling using KNIME-Workflows,
CEUR Workshop Proceedings 2535 (2020) 1–7.
[30] Y. B. Shapovalov, V. B. Shapovalov, V. I. Zaselskiy, TODOS as digital science-support
environment to provide STEM-education, Higher and secondary school pedagogy 52
(2019) 89–104.
[31] I. Slipukhina, S. Kuzmenkov, N. Kurilenko, S. Mieniailov, H. Sundenko, Virtual educational
physics experiment as a means of formation of the scientific worldview of the pupils,
CEUR Workshop Proceedings 2387 (2019) 318–333.
[32] Y. O. Modlo, S. O. Semerikov, P. P. Nechypurenko, S. L. Bondarevskyi, O. M. Bondarevska,
103
S. T. Tolmachev, The use of mobile Internet devices in the formation of ICT component
of bachelors in electromechanics competency in modeling of technical objects, CEUR
Workshop Proceedings 2433 (2019) 413–428.
[33] Y. Modlo, Y. V. Yechkalo, S. O. Semerikov, V. V. Tkachuk, Using technology of augmented
reality in a mobile-based learning environment of the higher educational institution,
Naukovi zapysky, Seriia: Problemy metodyky fizyko-matematychnoi i tekhnolohichnoi
osvity 11 (2017) 93–100.
[34] K. Vlasenko, O. Chumak, I. Lovianova, D. Kovalenko, N. Volkova, Methodical requirements
for training materials of on-line courses on the platform "higher school mathematics
teacher", E3S Web of Conferences 166 (2020). doi:10.1051/e3sconf/202016610011.
[35] L. Petrenko, S. Kravets, O. Bazeliuk, L. Maiboroda, I. Muzyka, Analysis of the current state
of distance learning in the vocational education and training institutions, E3S Web of
Conferences 166 (2020). doi:10.1051/e3sconf/202016610010.
[36] V. V. Yahupov, V. Y. Kyva, V. I. Zaselskiy, The methodology of development of information
and communication competence in teachers of the military education system applying the
distance form of learning, CEUR Workshop Proceedings 2643 (2020) 71–81.
[37] O. Y. Stryzhak, V. Gorborukov, O. Franchuk, M. Popova, Ontology of the choice problem
and its application in the analysis of limnological systems, Ecological safety and nature
management (2014) 172–183.
[38] Larysa Globa, M. Kovalskyi, O. Y. Stryzhak, Increasing Web Services Discovery Relevancy
in the Multi-ontological Environment, Advances in Intelligent Systems and Computing
342 (2019) 335–345. doi:10.1007/978-3-319-15147-2.
[39] L. Globa, S. Sulima, M. Skulysh, S. Dovgyi, O. Stryzhak, Architecture and Operation
Algorithms of Mobile Core Network with Virtualization, in: Mobile Computing,
IntechOpen, 2020, pp. 1–22. URL: https://www.intechopen.com/books/mobile-computing/
architecture-and-operation-algorithms-of-mobile-core-network-with-virtualization.
doi:10.5772/intechopen.89608.
[40] Gorborukov V., O. Y. Stryzhak, O. Franchuk, V. B. Shapovalov, Ontological representation
of the problem of ranking alternatives, Mathematical modeling in economics 4 (2018)
49–69. doi:10.1017/CBO9781107415324.004. arXiv:arXiv:1011.1669v3.
[41] V. Ivanov, Y. B. Shapovalov, V. Stabnikov, A. I. Salyuk, O. Stabnikova, M. ul Rajput Haq,
Barakatullahb, Z. Ahmed, Iron-containing clay and hematite iron ore in slurry-phase
anaerobic digestion of chicken manure, AIMS Materials Science 6 (2019) 807–817.
[42] Y. Shapovalov, V. Shapovalov, F. Andruszkiewicz, N. Volkova, Analyzing of main trends of
STEM education in ukraine using stemua.science statistics, CEUR Workshop Proceedings
2643 (2020) 448–461.
[43] V. Shapovalov, A. Atamas, Z. Bilyk, Y. Shapovalov, A. Uchitel, Structuring augmented
reality information on the stemua science, CEUR Workshop Proceedings 2257 (2018) 75–86.
URL: http://ceur-ws.org/Vol-2257/paper09.pdf.
104