=Paper=
{{Paper
|id=Vol-2879/paper03
|storemode=property
|title=A semantic structuring of educational research using ontologies
|pdfUrl=https://ceur-ws.org/Vol-2879/paper03.pdf
|volume=Vol-2879
|authors=Yevhenii B. Shapovalov,Viktor B. Shapovalov,Roman A. Tarasenko,Stanislav A. Usenko,Adrian Paschke
}}
==A semantic structuring of educational research using ontologies==
https://ceur-ws.org/Vol-2879/paper03.pdf
A semantic structuring of educational research using
ontologies
Yevhenii B. Shapovalov1 , Viktor B. Shapovalov1 , Roman A. Tarasenko1 ,
Stanislav A. Usenko1 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
This article is devoted to the presentation of the semantic interoperability of research and scientific results
through an ontological taxonomy. To achieve this, the principles of systematization and structuration
of the scientific/research results in scientometrics databases have been analysed. We use the existing
cognitive IT platform Polyhedron and extend it with an ontology-based information model as main
contribution. As a proof-of-concept we have modelled two ontological graphs, “Development of a rational
way for utilization of methane tank waste at LLC Vasylkivska poultry farm” and “Development a method
for utilization of methane tank effluent”. Also, for a demonstration of the perspective of ontological
systems for a systematization of research and scientific results, the “Hypothesis test system” ontological
graph has created.
Keywords
cloud technologies, ontology, educational research, taxonomy, systematization
1. Introduction
Now, more than ever, science affects all aspects of human life. Latest scientific developments are
often and quickly implemented in industry. However, the scientific results usually are presented
in human-readable form and not in a machine-readable, 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) [1]. Each section addresses a different
objective. The Introduction section motivates the research problem that was discovered or the
known facts about the problem; the Method section states what authors did to discover and
address the problem in a new solution, what they achieved as results in experiments is written
in the Discussion section, and what they had observed is discussed in the Results section.
CTE 2020: 8th Workshop on Cloud Technologies in Education, December 18, 2020, Kryvyi Rih, Ukraine
" sjb@man.gov.ua (Y. B. Shapovalov); svb@man.gov.ua (V. B. Shapovalov); tarasenko@man.gov.ua
(R. A. Tarasenko); farkry17@gmail.com (S. A. Usenko); paschke@inf.fu-berlin.de (A. Paschke)
~ 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=0000029045 (V. B. Shapovalov)
0000-0003-3732-9486 (Y. B. Shapovalov); 0000-0001-6315-649X (V. B. Shapovalov); 0000-0001-5834-5069
(R. A. Tarasenko); 0000-0002-0440-928X (S. A. Usenko); 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)
105
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 Paper, Argumentative (Persua-
sive) Research Paper, Definition Paper, Compare and Contrast Paper, Cause and Effect Paper,
Interpretative Experimental Research Paper, Survey Research. All the most common research
papers types are shown in table 1 [2].
Table 1
The most common research papers types
Types of the Research pa- Oriented amount Specific characteristics
pers of words required
Analytical Research Paper 3000+ Someone poses a question and then collect rele-
vant data from other researchers to analyse their
different viewpoints.
Argumentative (Persuasive) 3000+ The argumentative paper presents two sides of a
Research Paper controversial question in one paper.
Definition Paper 5000+ The definition paper describes facts or objective
arguments without using any personal emotion
or opinion of the author.
Compare and Contrast Pa- 5000+ Compare and contrast papers are used to analyse
per the difference between two viewpoints, authors,
subjects or stories.
Cause and Effect Paper 3000+ Cause and Effect Paper trace probable or expected
results 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 Pa- 3000+* This type of research paper describes a particular
per experiment in detail.
Survey Research Paper 5000+* This research paper demands the conduction of a
survey that includes asking questions to respon-
dents.
*
Depends on the purpose of the article and the requirements of the journal, institute, teacher
Most of the papers (but not all of them) nowadays are systemized by using scientometric
databases. However, educational research reports, which use scientific methods, have not been
systemized at all. Besides, scientist, unlike pupils, already know their field of research in detail
and can determine by themselves their research hypothesis and they can do further analyse it
by themselves. Students instead can’t do this. Automated informational tools can help students
in this scientific discovery and analysis tasks.
The scientific method is often used in an educational process during STEM approach by
providing educational researches. This approach is only recently applied in countries such as
Ukraine [3]. There are various school competitions for scientific works, such as the competition
on scientific articles of the Junior academy of sciences of Ukraine and international competitions
(for example, Intel ISEF). Also, the scientific method can be used during the process of creation
of thesis papers (for masters’ degree, bachelor’s degree, etc.), pupil’s research reports (for events
noted before), or in simpler, but more common form of essays. In addition, students can report
106
their results in form of scientific papers, if the level of quality of their work will be satisfactory
for the scientific requirements. An overview of the types of educational research reports works
are presented in table 2. The focus of this paper is on the systematization and processing
of educational research reports. The problem to be addresses is the lack of a Structuration
mechanism which complicates the automated processing of the reports.
Table 2
Types of the educational research reports
Types of the edu- Oriented Specific characteristics The event for which the re-
cational research required port was prepared
report amount of the
pages
Esse In general, up Is simple and very flexi- Classes, completions of
to 10-15 pages ble on the content school level
Research reports In general, Relatively static struc- Competitions of Junior
up to 30-100 ture; similar to IMRAD academy of sciences of
pages Ukraine and Intel ISEF
Scientific paper Declared by Declared by the source Publication in the journal
the source
Thesis papers In general, 40- Relatively static struc- Defence of the qualification
100 pages ture similar to IMRAD works
2. Literature review
To increase the convenience and efficiency of scientific data processing, structuration, and
systematization of research and scientific results, the active dissemination and use of different
scientometrics databases continues [4]. Specialized databases for structural science information
are an integral part of the information-support system for any scientist. Scientometrics is the
“quantitative study of science, communication in science, and science policy” [5] commonly
referred to as the “science of science”. Scientometrics is essential to help academic disciplines
understand various aspects of their research efforts, including (but not limited to) the produc-
tivity of their scholars [5, 6], the emergence of specializations [7], collaborative networks [8],
patterns of scientific communications [9], and quality of research products [10]. Metric studies
had developed as a subsidiary branch of Library and Information Science (LIS) over time [11].
In most cases, scientometrics models by using bibliometrics, which is a measure of the impact
of publications.
To increase the quality and performance of scientometrics the ten principles of the “Leiden
Manifesto of Scientometrics” have been stated [11]:
• Quantitative evaluation should support qualitative expert assessment.
• Measure performance against the research missions of the institution, group, or researcher.
• Protect excellence in locally relevant research.
• Keep data collection and analytical processes open, transparent and simple.
107
• Allow those evaluated to verify data and analysis.
• Account for variation by field in publication and citation practices.
• Assessment of individual research on a qualitative judgment of their portfolio.
• Avoid misplaced concreteness and false precision.
• Recognize the systemic effects of assessment and indicators.
• Scrutinize indicators regularly and update them.
Today, all existing scientometrics databases can be divided into two major groups: interna-
tional and national [11, 12, 13, 14, 15, 16, 17]. The most well-known international databases
are: Springer, Scopus, Web of Science, CiteseerX, Microsoft Academic, aminer, refseek, BASE
(Bielefeld Academic Search Engine), WorldWideSciense, JURN, Google Scholar, Google patent
and others. National databases incorporate a variety of bibliographic databases, and a variety
of library and university repositories. International scientometric databases are characterized
by a larger scale and mandatory support for various languages, including English. Also, a
characteristic feature of such databases is the availability and work with various special indices
that have international recognition for example h-index [18].
As scienctific publications continue to grow exponentially, also the amount of academic
databases and scientometrics databases increases, which supports gaining insights into the
structure and processes of science [16].In this case, many scientific publications devoted to the
principle of working scientometrics databases, and their number is growing. Thanks to them,
concepts such as “metadata” of scientific articles began to be actively used in scientometrics
[11, 12, 13, 14, 15, 16, 17]. Metadata is essential data about data providing information such
as titles, authors, abstracts, keywords, cited references, sources, and bibliography, and other
data. Metadata do not substitute the corresponding article, but it explicitly describes valuable
information about the article.
By using of scientometrics systems, the contributions of researchers in the field of informatics
and scientometrics were previously quantified [13]. The principal metadata indicators are: the
indicators and citation indices of journals, the number of authors, the number of the publication
and the degree of cooperation based on affiliation data. The disadvantage of this research is
that it is devoted only to scientific articles. The authors noted that their study could not touch
student’s and pupil’s research report because there is no single database where they are all
located [13].
The application of the principles of the “Leiden Manifesto of Scientometrics” is stated and
substantiated, which provides for transparent monitoring and support of research and encour-
ages constructive dialogue between the scientific community and the public. In this work, the
bibliometric base, which corresponds to principles of the “Leiden Manifesto of Scientometrics”
has been created. The proposed bibliometric centre did not address the systematization of
students and pupils’ research reports, but the authors noted the necessity of involvement of
students’ and pupils’ research reports in their bibliometric centre [12].
The approach of co-word analysis has been introduced and its application in scientometrics
is substantiated in [14]. The trends and patterns of scientometrics in journals has been revealed
by measuring the association strength of selected keywords which represent the produced
concept and idea in the field of scientometrics. Also, the authors have developed a web system
108
for extraction of keywords from the title and abstract of the article manually. However, the web
system proposed by them cannot work with research reports of students and pupils.
Another concept of analysis is iMetrics or “information metrics”. Its application in sciento-
metrics is substantiated in [19]. iMetrics is devoted to the scientometrics of scientific journals in
the field of informatics. The authors note the possibility of applying their approach for system-
atization of the scientific works of students and pupils. The research related to scientometrics
databases is shown in table3.
Table 3
The research related to scientometrics databases
Subject of study The general result of the study Authors
Citation indices of journals, The contributions of researchers in K. R. Mulla
number of authors of the the field of informatics and sciento-
publication their affiliation metrics
Principles of the “Leiden Stated and substantiated of , “Leiden L. Kostenko, A. Zhabin,
Manifesto of Scientomet- Manifesto of Scientometrics” A. Kuznetsov, T. Lukashevich,
rics” E. Kukharchuk, T. Simonenko
Co-word analysis The trends and patterns of sciento- S. Ravikumar, A. Agrahari,
metrics in the journals were revealed S. N. Singh
iMetrics (“information met- iMetrics scientometric system had S. Milojevic, L. Leydesdorff
rics”) provided
Previously, ontological graphs were used to systematize scientific articles [20, 21, 22, 23].
Systematization and structuration in such graphs is based on different approaches such as
using of scientific article recommendation system [20], Scientific Articles Tagging system
[21], machine learning [22], automatic summarization [23]. Also, ontologies can be to provide
interoperability through semantic technologies [24]. However, none of the proposed ontological
approaches for systematization and structuration is addressing the structuration of research
reports of students and pupils.
None of the scientometrics database systems previously proposed [11, 12, 13, 14, 15, 16, 17]
can offer a universal solution 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 parameters, that are useful for processing information about scientific
works. These parameters 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, existing solutions
do not allow to compare research reports between each other.
This work aims to propose and justify the use of an ontological 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 deprived of
the functionality of current scientometrics systems and will meet the Leiden Manifesto for
Scientometrics.
We propose to use the existing cognitive IT-platform Polyhedron as technical basis for solving
this problem. The core of the Polyhedron system consists of advanced and improved functions
of the TODOS IT-platform described in previous works. Polyhedron is a multi-agent system
109
which allows for transdisciplinary and acts as an interactive component in any educational and
scientific research [25]. Besides, the cognitive IT-platform Polyhedron contains a function for
comparison with standards which is called auditing [25, 26, 27]. Polyhedron provides: semantic
web support, information systematization and ranking [28] transdisciplinary support, internal
search [29] has all advantages of ontological interface tools [30], and the construction of all
chains of the process of transdisciplinary integrated interaction is ensured [31]. Due to active
states are hyper-ratio plural partial ordering [32, 33], the cognitive IT-platform Polyhedron is an
innovative IT technology for ontological management of knowledge and information resource.
The user of the Polyhedron IT system has an opportunity to use an internal search function that
is more protected and reliable compared to the external one, because it provides information
created by experts.
Also, the proposed solution for the structuration of educational and research projects can be
used together with other modern developments in the educational field, like a virtual educational
experiment [34, 35, 36, 37], different tools to provide development of ICT [38, 39, 40, 41], the use
of mobile Internet devices [42, 43, 44, 45], using the technology of augmented reality education
[46, 47, 48, 49], online courses [50, 51, 52, 53], distance learning in vocational education and
training institutions [54, 55, 56, 57], educational and scientific environments [58, 29, 59, 60].
3. Materials and methods
3.1. Ontology creation mechanism
To create ontologies in Polyhedron, Google Sheets were used to collect and structure the
information (see example in figure 1). The sheets with research report data (structure file and
numeric/semantic data file) have been downloaded and saved in .xls format. The files have
been loaded to “editor.stemua.science”, which is part of Polyhedron. After that, the generation
of the graph nodes (in .xls) with its characteristics using the data structures in the file have
been carried out. The obtained graphs have been saved in .xml format and located in the
database. The graphs have been filled by semantic and numeric information for ranking and
filtering. Ontological edges (relations) have been formed using predicate equations, as described
previously in [31].
3.2. Ranking tools
Taking into account that e.g. proposed reports “A” and “B” are technical, the results of the
reported works can be used to provide analysis of the rationality of the implementation proposed
in the concrete project. For instance, to provide it, research reports “A” and “B” were also
compared with each other using ranking tool applying the following criteria: “Short-term
economic perspective”, “Long-term economic prospects”. For creating a ranking the ontologies
have used the module “Alternative” which is described in our previous works [28]. To provide
this ranking, the nodes of a graph have been filled with semantic data grouped in semantic
classes. The ranking uses grade scale from one to ten point to underline the importance
coefficient.
110
Figure 1: Google sheet with data.
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 of payback period with 4 points, 6–10 yeas of payback period with
5 points and with 1–5 years were evaluated as 6-10 points, respectively, by the “Economic
attractiveness” criterion. A detailed evaluation for projects with 1–5 years is provided, due
to it’s utmost interest for the investor’s “payback time” , which determines the expediency of
investment.
3.3. Auditing tools
To provide an audit of hypothesis of work “A” and “B”, the “standard” graph (with which the
comparison 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 before. For
the “standard” graph, each parameter was presented in a separate node. The content of this
ontological graph “standard” is updates and supplemented constantly.
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 names of the nodes of the “standard” graph in order
to enable interaction between graphs.
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 represented as an ontology.
After structuration, it is possible to represent the reports’ content in simpler to understand
111
presentation form. Besides, most results can be domain specific for each industry, and if the
current standards are correctly identified, these values will be easy to compare. Also, most
research in one field often use the same equipment, materials, chemicals, standard methods
of analysis, literature, etc., which allow comparing these works with each other and correctly
structuration them.
However, the main advantage of the proposed approach (besides structuration of the research)
is the processing of results in terms of separated result parameters of the reports. This supports
data analysis, further processing using ranking, and semantic data interoperability. The separa-
tion of numeric data and its location metadata class is possible due to the addresses of the same
field, that is describing the process using same (or similar) parameters of the process description
and result parameters description. For example, for most reports on anaerobic digestion, the
process parameters are on 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 process, destruction process etc. [61].
As all research reports will be presented in a simplified form, this approach will be especially
relevant for pupils and novice researchers with further potential use in the educational process
or to simplify the literature review process for the new educational research.
4.1. Description of scientific works used to provide structuration
As an example, the object of the study of research report “A” is the disposal of anaerobic effluent.
The subject of the research of the report is the Cultivation of Chlorella Vulgaris microalgae
on effluent obtained after methane fermentation. The study aims to develop a method of
growing Chlorella Vulgaris in effluent after methane fermentation. The practical significance
of this scientific work is the results of this work, which will contribute to the spread of biogas
technologies. Also, the proposed approach makes it possible to increase the economic benefits
from the utilization of chicken manure by converting the anaerobic digestion effluent into
microalgae, that have a wide range of applications. The scientific novelty of that research report
is a method of utilization of anaerobic digestion effluent by using microalgae, also had obtained
cultures of Chlorella Vulgaris that had adapted to the anaerobic digestion effluent. The working
hypothesis was that the effluent obtained after anaerobic digestion can be used as a nutrient
medium for microalgae Chlorella Vulgaris.
The object of the study of the research report “B” is the disposal of anaerobic digestion effluent.
The subject of the research is the processing of anaerobic digestion effluent into humates by the
autocatalytic catalysis method. The study aims to establish regularities of processing of the solid
fraction, which had obtained during the process of methane fermentation of chicken manure
by autocatalytic catalysis method. The practical significance of this scientific work is that the
study indicates the possibility of acquiring salts of humic and fulvic acids by the autocatalytic
catalysis method. This approach makes it possible to increase the economic benefits from the
disposal of chicken manure by converting the anaerobic digestion effluent into a more valuable
product with a wide range of applications. Its scientific novelty is that potassium hu- mate
had firstly obtained from anaerobic digestion effluent and for the first time the efficiency of
receiving humates from the solid fraction of anaerobic digestion had investigated and the main
regularities of the process determined. The working hypothesis was that the solid fraction of
112
methane fermentation of chicken manure can be recycled by the autocatalytic catalysis method.
For both research report “A” and “B”, as a substrate for anaerobic digestion have used the
chicken manure from the same poultry farm. In this case, chicken manure and its effluent,
which has obtained by anaerobic digestion, were analysed by the same methods and indicators.
Such indicators were: “ash and dry content”, “Determination of volatile fatty acids content” (in
terms of acetic acid), “Determination of ammonium nitrogen content with Nessler’s reagent”.
The equipment which has used to determine these indicators was also the same. Therefore,
has considered how these works can be structured and integrated by using of the cognitive
IT-platform Polyhedron. All examples of the usage ontological nodes the obtained graphs for
further potential information processing are presented in table 4.
4.2. Structuration of the scientific works using ontologies
For the presentation of possibilities and systematization of the research report we have applied a
ontological taxonomy for students’ works “A” and “B”. The general view of the obtained graphs
is shown in figure 2 [31].
Figure 2: The general view of the (a) research report “A” (b) research report “B” ontological graph.
A separate node called “Abstract” has been created, 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 form of the
attributes. All metadata have been used to provide filtering and ranking.
The “Materials and methods” node, which contains all the materials was used to perform the
experiments. Every approach has been divided into the separate attribute of the node. This
allows concentrating the reader’s attention, and it helps to process the data with each other. In
further researchers, this mechanism will be described in detail. The general view of both works’
“Material and Methods” node is shown in figure 3 [31].
For each ontological node that duplicate sections of the research report, and that contain
specific indicators after analysing, additional separate leaf nodes with these results have created.
In this leaf node, all the issues are held in the form of semantic and numeric data. These results
113
Table 4
Examples of the usage of the educational research element in ontology
Element Example The role of the node in Using of the data
of the ed- the resulting graph
ucational
research
Title Node: “Development a method for Parent node Used only for structuration
utilization of anaerobic digestion ef-
fluent”
Object Node: Abstract Located in Abstract Used for the audit; to provide
Class: Object node; each object pre- literature review; to link reports
(object is only one per report) sented as attribute for each other with same data;
Value: Anaerobic digestion; to identify novelty and plagia-
Value: Microalgae’s growth rism
Value: Disposal of the waste
Subject Node: Abstract Located in Abstract Same as previous
Class: Subject node; each object pre-
Value: The processing of anaerobic sented as attribute
digestion effluent into humates by
the autocatalysis method
Hypothesis Node: Abstract Located in Abstract Same as previous
Class: Hypothesis node; each object pre-
Value: Effluent obtained after sented as attribute
anaerobic digestion can be used as
a nutrient medium for microalgae
Chlorella Vulgaris
Keywords Node: Abstract Located in Abstract Same as previous
Class: Keywords node; each object pre-
Value1: Biogas; sented as attribute
Value2: Anaerobic digestion
Value3: Microalgae
Sections, Node: Introduction; Each section presented Used for representing of the
Abstract, Class1: Text; in separated nodes; all main text of the educational re-
Introduc- Value1: text itself; text is presented in sep- ports; structuration and naviga-
tion Class2: Biogas production in litera- arate class of metadata, tion
ture, ml/g of VS; based on type of data
Value2: 368;
Class3: methane content, % ;
Value3: 59
Materials Node: Materials and methods Located single node; Used to provide links between
and meth- Class1: Method1; each method is sepa- the reports used same method
ods Value1: Desorption1; rated class of metadata by indexing and search
Class2: Method2;
Value2: Desorption2
Concrete Node: Results Located a in separate Used for the creation of the sin-
results Class1: pH; node; each parameter is gle ranking tool to systemize re-
and pa- Value1: 7.3; separated class of meta- sults from same field
rameters Class2: Decomposition, %; data
of the Value2: 87
research
Economic Node:Economic data Located the separate Used to provide comparison of
data Class: Payback period, years; node; payback period the approaches to assess invest-
114
Value: 5.3 presented in metadata ment attractiveness
References Node: Li et al. 2018, Chen 2003, Each report (paper) lo- Used to link reports used same
Sergienko et al. 2016 cated in separate node reference with each other
Figure 3: The general view of a) research report “A” b) research report “B” “Materials and methods”
node.
are automatically 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 analysing.
5. Information processing of the research report using
Polyhedron tools
5.1. Using an audit tool to test a hypothesis
The audit tool [25, 26, 27] can be used to compare the hypotheses, subjects, objects of research,
keywords, and other parameters of the research reports. To demonstrate the capabilities of the
115
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 structure without branches with the parent node
being named “Abstract”. The child nodes duplicate metadata from the “Abstract” node of the
research reports “A”.
The “comparison” ontology has been created with the child nodes which contain the following
hypothesis: the effluent obtained after anaerobic digestion can be used as a nutrient medium
for microalgae Spirulina Platensis (hypothesis 1), the effluent obtained after anaerobic digestion
can be used as a nutrient medium for microalgae Chlorella Vulgaris (hypothesis 2), the effluent
obtained after anaerobic digestion cannot use it as a nutrient medium for microalgae Chlorella
Vulgaris (hypothesis 3). The hypothesis 2 node also contain some metadata. This ontology also
had a simple structure without branches with the parent node is the “Hypothesis test system”.
The general view of the obtained ontology of the comparison and the ontology of the standard
in taxonomic form is shown in figure 5.
Figure 5: General view of in the taxonomic form the ontology of the “comparing” (a) and (b) the
ontology of the “standard”.
Using the function of the audit the system has checked the hypothesis to be true or false.
Those indicators which do not correspond to the standard have been colored by red. Thus, this
solution will allow not only to test the hypothesis of these scientific works, but also to check
other metadata that have already been set by using information from the “Abstract” node (see
figure 6).
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
criteria “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 to the criterion “Economic attractiveness”. This parameter
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 results. In case, if there will be a large amount of the data, the instrument, will be
useful 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
116
Figure 6: General view of the audit results in the “Hypothesis test system” ontology.
the educational research, which will make the tool more functional. The general view of the
ranking result is presented in figure 7.
6. Discussion
The proposed database follows the “Leiden Manifesto of Scientometrics”. In the obtained
ontological database quantitative evaluation can be supported by qualitative expert assessment.
Additionally, this ontological database can unite the research missions of the institution, group,
or researcher and protect excellence in internally relevant research. The ontological form of
research reports can keep data collection and analytical processes open, transparent, and simple.
Because all metadata is contained in a separate node that can be expanded and supplemented.
Thus the obtained ontological database can also account for variations, e.g. in publication and
citation practices and it can provide a base assessment of individual researchers in a qualitative
judgment of their portfolio. 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 assessment and indicators. In addition, in the obtained ontological database
indicators can be scrutinized regularly and updated. Furthermore, the proposed ontology-based
117
Figure 7: General view of the ranking result.
research reports can be integrated in a single environment – ontology repositories, as it was
proposed before [62].
The process starts from the paper creation, for this stage we can use various text editors,
for example, word or google doc. 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 in the graph, in our occasion it is the IT
Platform Polyhedron. And last, but not least it is possible to use the “Alternative” system, which
includes Audit, Filtering and Ranking instruments. All proposed instruments are illustrated in
the workflow diagram in figure 8.
Figure 8: Workflow diagram of the creation of structured ontologies on scientific reports and their
processing.
118
7. Conclusions
An ontological approach for the systematization of scientific works has been proposed, which
also ensures their interoperability. A method of research reports structuration using digital
taxonomies (ontologies) has been developed. It supports using the native structure of the reports
to define hierarchical relations of the nodes. Concrete parameters were added as metadata
(semantic, numeric, pictures and links) of the nodes to provide processing using Polyhidron
tools. Ranging and filtering were used for semantic and numeric metadata processing. Obtained
results provide interoperability between different research reports (including educational). The
obtained ontological approach follows the “Leiden Manifesto of Scientometrics”.
Further research will be devoted to provide even better interoperability between research
works by providing generation of one single taxonomy that provides hierarchization by same
methods, literature and results of the reports and its processing using both, methods proposed
in the research and newly developed ones.
References
[1] L. Oriokot, W. Buwembo, I. Munabi, S. Kijjambu, The introduction, methods, results and
discussion (IMRAD) structure: A Survey of its use in different authoring partnerships in
a students’ journal, BMC Research Notes 4 (2011) 250. URL: http://www.biomedcentral.
com/1756-0500/4/250. doi:10.1186/1756-0500-4-250.
[2] Paperpale, Types of research papers, 2020. URL: https://paperpile.com/g/
types-of-research-papers/.
[3] 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. URL: http://ceur-ws.org/Vol-2643/paper26.pdf.
[4] S. Semerikov, V. Pototskyi, K. Slovak, S. Hryshchenko, A. Kiv, Automation of the export
data from Open Journal Systems to the Russian Science Citation Index, CEUR Workshop
Proceedings 2257 (2018) 215–226.
[5] A. Ramesh Babu, Y. P. Singh, Determinants of research productivity, Scientomet-
rics 43 (1998) 309–329. URL: http://link.springer.com/10.1007/BF02457402. doi:10.1007/
BF02457402.
[6] G. Abramo, C. A. D’Angelo, F. D. Costa, University-industry research collaboration: A
model to assess university capability, Higher Education 62 (2011) 163–181. doi:10.1007/
s10734-010-9372-0.
[7] M. Pianta, D. Archibugi, Specialization and size of scientific activities: A bibliometric anal-
ysis of advanced countries, Scientometrics 22 (1991) 341–358. doi:10.1007/BF02019767.
[8] M. Newman, The structure of scientific collaboration networks, in: The Structure and
Dynamics of Networks, volume 9781400841, Princeton University Press, Princeton, 2011,
pp. 221–226. doi:10.1515/9781400841356.221.
[9] T. Braun, W. Glänzel, A. Schubert, Publication and cooperation patterns of the au-
thors of neuroscience journals, Scientometrics 51 (2001) 499–510. doi:10.1023/A:
1019643002560.
119
[10] S. M. Lawani, Some bibliometric correlates of quality in scientific research, Scientometrics
9 (1986) 13–25. doi:10.1007/BF02016604.
[11] A. A. Khasseh, F. Soheili, H. S. Moghaddam, A. M. Chelak, Intellectual structure of
knowledge in iMetrics: A co-word analysis, Information Processing & Management 53
(2017) 705–720. doi:10.1016/j.ipm.2017.02.001.
[12] L. Kostenko, A. Zhabin, A. Kuznetsov, T. Lukashevich, E. Kukharchuk, T. Simonenko,
Scientometrics: A Tool for Monitoring and Support of Research, Science and Science of
Science (2015) 88–94.
[13] K. R. Mulla, Identifying and mapping the information science and scientometrics analysis
studies in India (2005-2009): A bibliometric study, Library Philosophy and Practice (2012)
1–18.
[14] S. Ravikumar, A. Agrahari, S. N. Singh, Mapping the intellectual structure of scientometrics:
a co-word analysis of the journal Scientometrics (2005–2010), Scientometrics 102 (2015)
929–955. doi:10.1007/s11192-014-1402-8.
[15] I. Pavlovskiy, Using Concepts of Scientific Activity for Semantic Integration of Publications,
Procedia Computer Science 103 (2017) 370–377. doi:10.1016/j.procs.2017.01.123.
[16] B. E. Perron, B. G. Victor, D. R. Hodge, C. P. Salas-Wright, M. G. Vaughn, R. J. Taylor,
Laying the Foundations for Scientometric Research: A Data Science Approach, Research
on Social Work Practice 27 (2017) 802–812. doi:10.1177/1049731515624966.
[17] M. C. Ramirez, R. A. R. Devesa, A scientometric look at mathematics education from
Scopus database, Mathematics Enthusiast 16 (2019) 37–46.
[18] O. Kinouchi, L. D. Soares, G. C. Cardoso, A simple centrality index for scientific social
recognition, Physica A: Statistical Mechanics and its Applications 491 (2018) 632–640.
doi:10.1016/j.physa.2017.08.072.
[19] S. Milojević, L. Leydesdorff, Information metrics (iMetrics): a research specialty
with a socio-cognitive identity?, Scientometrics 95 (2013) 141–157. doi:10.1007/
s11192-012-0861-z.
[20] M. Amami, R. Faiz, F. Stella, G. Pasi, A graph based approach to scientific paper recom-
mendation, in: Proceedings - 2017 IEEE/WIC/ACM International Conference on Web
Intelligence, WI 2017, 2017, pp. 777–782. doi:10.1145/3106426.3106479.
[21] D. Boughareb, A. Khobizi, R. Boughareb, N. Farah, H. Seridi, A Graph-Based Tag Rec-
ommendation for Just Abstracted Scientific Articles Tagging, International Journal of
Cooperative Information Systems 29 (2020) 2050004. URL: https://www.worldscientific.
com/doi/abs/10.1142/S0218843020500045. doi:10.1142/S0218843020500045.
[22] N. Perraudin, Graph-based structures in data science : fundamental limits and applications
to machine learning, Ph.D. thesis, 2017. URL: https://infoscience.epfl.ch/record/227982?ln=
en. doi:10.5075/epfl-thesis-7644.
[23] D. Parveen, A Graph-based Approach for the Summarization of Scientific Articles, Ph.D.
thesis, 2018.
[24] R. Alnemr, A. Paschke, C. Meinel, Enabling reputation interoperability through semantic
technologies, ACM International Conference Proceeding Series (2010). doi:10.1145/
1839707.1839723.
[25] 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
120
management (2014) 172–183.
[26] L. 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.
[27] 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.
[28] V. Gorborukov, 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.
[29] 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. URL: http://ceur-ws.org/Vol-2433/paper15.pdf.
[30] 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.
[31] V. Velichko, M. Popova, V. Prikhodnyuk, O. Y. Stryzhak, TODOS is an IT platform for the
formation of transdisciplinary information environments, Weapons systems and military
equipment 1 (2017) 10–19.
[32] R. Volckmann, Transdisciplinarity: Basarab Nicolescu Talks with Russ Volckmann, Lancet
Neurology 6 (2007) 76. doi:10.1016/S1474-4422(07)70211-9.
[33] B. Nicolescu, A. Ertas, Transdisciplinary, Theory Practice, 2013.
[34] 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.
[35] P. Nechypurenko, T. Selivanova, M. Chernova, Using the cloud-oriented virtual chemical
laboratory VLab in teaching the solution of experimental problems in chemistry of 9th
grade students, CEUR Workshop Proceedings 2393 (2019) 968–983.
[36] O. Lavrentieva, I. Arkhypov, O. Kuchma, A. Uchitel, Use of simulators together with virtual
and augmented reality in the system of welders’ vocational training: Past, present, and
future, CEUR Workshop Proceedings 2547 (2020) 201–216.
[37] O. Bondarenko, O. Pakhomova, W. Lewoniewski, The didactic potential of virtual informa-
tion educational environment as a tool of geography students training, CEUR Workshop
Proceedings 2547 (2020) 13–23.
[38] Y. Modlo, S. Semerikov, E. Shmeltzer, Modernization of professional training of electrome-
chanics bachelors: ICT-based Competence Approach, CEUR Workshop Proceedings 2257
(2018) 148–172.
[39] E. Fedorenko, V. Velychko, A. Stopkin, A. Chorna, V. Soloviev, Informatization of education
as a pledge of the existence and development of a modern higher education, CEUR
Workshop Proceedings 2433 (2019) 20–32.
[40] P. Nechypurenko, V. Stoliarenko, T. Starova, T. Selivanova, O. Markova, Y. Modlo,
E. Shmeltser, Development and implementation of educational resources in chemistry
121
with elements of augmented reality, CEUR Workshop Proceedings 2547 (2020) 156–167.
[41] V. Tkachuk, Y. Yechkalo, S. Semerikov, M. Kislova, V. Khotskina, Exploring student uses of
mobile technologies in university classrooms: Audience response systems and development
of multimedia, CEUR Workshop Proceedings 2732 (2020) 1217–1232.
[42] Y. O. Modlo, S. O. Semerikov, P. P. Nechypurenko, S. L. Bondarevskyi, O. M. Bondarevska,
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.
[43] Y. Modlo, S. Semerikov, S. Bondarevskyi, S. Tolmachev, O. Markova, P. Nechypurenko,
Methods of using mobile Internet devices in the formation of the general scientific compo-
nent of bachelor in electromechanics competency in modeling of technical objects, CEUR
Workshop Proceedings 2547 (2020) 217–240.
[44] Y. Modlo, S. Semerikov, R. Shajda, S. Tolmachev, O. Markova, P. Nechypurenko, T. Seliv-
anova, Methods of using mobile internet devices in the formation of the general profes-
sional component of bachelor in electromechanics competency in modeling of technical
objects, CEUR Workshop Proceedings 2643 (2020) 500–534.
[45] V. Tkachuk, Y. Yechkalo, S. Semerikov, M. Kislova, Y. Hladyr, Using Mobile ICT for On-
line Learning During COVID-19 Lockdown, in: A. Bollin, V. Ermolayev, H. C. Mayr,
M. Nikitchenko, A. Spivakovsky, M. Tkachuk, V. Yakovyna, G. Zholtkevych (Eds.), Informa-
tion and Communication Technologies in Education, Research, and Industrial Applications,
Springer International Publishing, Cham, 2021, pp. 46–67.
[46] A. Striuk, M. Rassovytska, S. Shokaliuk, Using Blippar augmented reality browser in the
practical training of mechanical engineers, CEUR Workshop Proceedings 2104 (2018)
412–419.
[47] S. Zelinska, A. Azaryan, V. Azaryan, Investigation of opportunities of the practical applica-
tion of the augmented reality technologies in the information and educative environment
for mining engineers training in the higher education establishment, CEUR Workshop
Proceedings 2257 (2018) 204–214.
[48] P. Nechypurenko, T. Starova, T. Selivanova, A. Tomilina, A. Uchitel, Use of augmented
reality in chemistry education, CEUR Workshop Proceedings 2257 (2018) 15–23.
[49] V. Shapovalov, Y. Shapovalov, Z. Bilyk, A. Megalinska, I. Muzyka, The Google Lens analyz-
ing quality: An analysis of the possibility to use in the educational process, CEUR Workshop
Proceedings 2547 (2020) 117–129. URL: http://www.ceur-ws.org/Vol-2547/paper0.
[50] 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.
[51] K. Vlasenko, S. Volkov, I. Sitak, I. Lovianova, D. Bobyliev, Usability analysis of on-line
educational courses on the platform "Higher school mathematics teacher", E3S Web of
Conferences 166 (2020) 10012. doi:10.1051/e3sconf/202016610012.
[52] K. Vlasenko, D. Kovalenko, O. Chumak, I. Lovianova, S. Volkov, Minimalism in designing
user interface of the online platform “Higher school mathematics teacher”, CEUR Workshop
Proceedings 2732 (2020) 1028–1043.
[53] 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
122
distance form of learning, CEUR Workshop Proceedings 2643 (2020) 71–81.
[54] S. Shokaliuk, Y. Bohunenko, I. Lovianova, M. Shyshkina, Technologies of distance learning
for programming basics on the principles of integrated development of key competences,
CEUR Workshop Proceedings 2643 (2020) 548–562.
[55] M. Syvyi, O. Mazbayev, O. Varakuta, N. Panteleeva, O. Bondarenko, Distance learning as
innovation technology of school geographical education, CEUR Workshop Proceedings
2731 (2020) 369–382.
[56] D. Y. Bobyliev, E. V. Vihrova, Problems and prospects of distance learning in teaching
fundamental subjects to future mathematics teachers, Journal of Physics: Conference
Series 1840 (2021) 012002. URL: https://doi.org/10.1088/1742-6596/1840/1/012002. doi:10.
1088/1742-6596/1840/1/012002.
[57] K. Polhun, T. Kramarenko, M. Maloivan, A. Tomilina, Shift from blended learn-
ing to distance one during the lockdown period using Moodle: test control of stu-
dents’ academic achievement and analysis of its results, Journal of Physics: Confer-
ence Series 1840 (2021) 012053. URL: https://doi.org/10.1088/1742-6596/1840/1/012053.
doi:10.1088/1742-6596/1840/1/012053.
[58] P. Merzlykin, M. Popel, S. Shokaliuk, Services of SageMathCloud environment and their
didactic potential in learning of informatics and mathematical disciplines, CEUR Workshop
Proceedings 2168 (2017) 13–19.
[59] Y. Shapovalov, V. Shapovalov, V. Zaselskiy, TODOS as digital science-support environment
to provide STEM-education, CEUR Workshop Proceedings 2433 (2019) 232–245. URL:
http://ceur-ws.org/Vol-2433/paper14.pdf.
[60] V. Pererva, O. Lavrentieva, O. Lakomova, O. Zavalniuk, S. Tolmachev, The technique of
the use of Virtual Learning Environment in the process of organizing the future teachers’
terminological work by specialty, CEUR Workshop Proceedings 2643 (2020) 321–346.
[61] 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 (2020) 821–832.
[62] 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.
123