=Paper=
{{Paper
|id=Vol-3083/paper278
|storemode=property
|title=Ontology-based learning environment model of scientific studies
|pdfUrl=https://ceur-ws.org/Vol-3083/paper278.pdf
|volume=Vol-3083
|authors=Roman A. Tarasenko,Stanislav A. Usenko,Yevhenii B. Shapovalov,Viktor B. Shapovalov,Adrian Paschke,Iryna M. Savchenko
|dblpUrl=https://dblp.org/rec/conf/icteri/TarasenkoUSSPS21
}}
==Ontology-based learning environment model of scientific studies==
https://ceur-ws.org/Vol-3083/paper278.pdf
Ontology-based learning environment model of
scientific studies
Roman A. Tarasenko1 , Stanislav A. Usenko1 , Yevhenii B. Shapovalov1 ,
Viktor B. Shapovalov1 , Adrian Paschke2 and Iryna M. Savchenko1
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
Nowadays, there is a wide variety of scientific articles. Due to this fact, it is hard to read and be familiar
with all of them. Also, it is hard for a young scientist to understand the complicated terms and methods that
are used in a specific research domain. This problem was partially solved by bibliographic management
software and other specific software. This article is devoted to the development of an approach for
structuration and processing sets of studies using the IT Platform Polyhedron using an ontology-based
hierarchical model. In its structure, the ontological graph is complex because it has additional branches
from child nodes. The basis of our solution was IMRAD which has been represented in the view of
nodes. Those nodes have been connected with specific representations of IMRAD elements. Specific
articles have been represented in the view of leaf nodes. That could help to use the taxonomies for the
structuration of the articles. Each data block is in the form of separate attributes of the ontological node.
The proposed solution allows to obtain structured sets of studies and to separate their characteristics.
Thus, the proposed ontology provides the possibility to view all methods, measured parameters, etc. of
the studies in a graph node structure and use them to find the studies where they were used.
Keywords
cognitive IT-platform Polyhedron, ontology, ontological tool, scientific studies, scientific reports, learning
environments
1. Introduction
Usage of information technologies (IT) in various fields of research activities and the capa-
bility of software support in science to automatically classify and structure information, e.g.,
CoSinE 2021: 9th Illia O. Teplytskyi Workshop on Computer Simulation in Education,
co-located with the 17th International Conference on ICT in Education, Research, and Industrial Applications:
Integration, Harmonization, and Knowledge Transfer (ICTERI 2021), October 1, 2021, Kherson, Ukraine
" tarasenko@man.gov.ua (R. A. Tarasenko); sjb@man.gov.ua (Y. B. Shapovalov); svb@man.gov.ua
(V. B. Shapovalov); paschke@inf.fu-berlin.de (A. Paschke); SavchenkoI@nas.gov.ua (I. M. Savchenko)
~ https://www.scopus.com/authid/detail.uri?authorId=57211134149 (R. A. Tarasenko);
https://www.scopus.com/authid/detail.uri?authorId=57224619975 (S. A. Usenko);
http://www.nas.gov.ua/EN/PersonalSite/Pages/default.aspx?PersonID=0000026333 (Y. B. Shapovalov);
https://www.nas.gov.ua/EN/PersonalSite/Pages/default.aspx?PersonID=0000029045 (V. B. Shapovalov);
https://dblp.org/pid/24/2942.html (A. Paschke);
https://www.nas.gov.ua/EN/PersonalSite/Statuses/Pages/default.aspx?PersonID=0000020650 (I. M. Savchenko)
0000-0001-5834-5069 (R. A. Tarasenko); 0000-0002-0440-928X (S. A. Usenko); 0000-0003-3732-9486
(Y. B. Shapovalov); 0000-0001-6315-649X (V. B. Shapovalov); 0000-0003-3156-9040 (A. Paschke); 0000-0002-0273-9496
(I. M. Savchenko)
© 2022 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)
43
in publication data, becomes increasingly important. Nowadays, there are huge amounts of
research data available that isn’t structured, e.g., publications, presentations, etc. It is com-
plicated for young researchers and scientists to use such large amounts of publication data.
During the research process, young scientists are looking for, e.g., examples of research methods
and parameters. However, this task is challenging at their early stage of the scientific career.
Such literature search and analysis problems (e.g. state of art analysis) are challenging for
every scientist (including youth, school researchers) during the process of preparing papers
and reports. For instance, according to Lens.org, the number of articles on biogas in 2002 was
approximately 134, then in 2014, the number grew up to almost 1164, as shown in figure 1.
Figure 1: Dynamic of the number of the papers on biogas.
So, it seems relevant to provide a solution that can simplify processing and informa-
tion/knowledge extraction in scientific publications. There are two hypotheses in our study.
The first one is about structuring and digitalization of the data, which can simplify finding the
details about the research method. The second one is about structuring the results of previous
studies, which can be represented as data of the informational system. Previously, this goal was
partially achieved using metadata for data processing. In this paper we further contribute with
a semantic ontology and more expressive semantic metadata approach.
1.1. Using metadata to provide data management in science publications
To support publication data management, it is relevant to use metadata about each paper. This
metadata represents the data about the publication. In this case, relevant information about
each specific publication can be represented by the metadata. Metadata can include, e.g., contact
information, year of publication, author details, instrument and protocol information, survey
tool details and much more [1].
44
For instance, reference management software maintains a database of articles and creates
bibliographies and reference lists for the written works. This software simplifies the record of
metadata. There are several popular reference management software, for example, Refworks,
Mendeley, EndNote and Zotero [2, 3, 4, 5]. All of these managers can save profiles, build a
database of citations, save PDF files and extract metadata from them, import references from
library catalogues, websites, and other citation managers [6, 7, 8].
However, these systems only use limited metadata vocabularies without expressive semantic
models. For instance, such systems do not support metadata concepts such as “Results”, “Mate-
rials and methods”, “References” etc. All these systems do not provide a systematic approach,
they are not completely semantically structured and not hierarchical.
1.2. Methods for automatic literature review processing
There are existing different types of metadata information that we can use to structure the
articles, for example, by the relation to source, by the function, by the purpose, by the language,
by the time:
• By the “Relation to Source”: During this method, the user defines the type of text that
can be included in a classification program. A classification that is composed of extracts
having exact sentences of a source document is known as an extractive summary. That is
the simplest type of classifier.
• By the “Function”: with this method, user can use any helpful and relevant information
from source documents, for example, an abstract of a scientific article or the reviewers’
opinion on the quality of work.
• By the “Purpose”: This method structures the article by its purpose or main idea. The user
needs to write down the general-purpose or sense of the text in the program by himself.
• By the “Language”: A classifier can be monolingual or multilingual. The monolingual
classifier uses only one language and produces an output classification in the same lan-
guage as the input document whereas, the multilingual classifier uses multiple languages
and gives an output classification in one of the languages from the input document.
• By the “Time”: It is possible to arrange the articles by the time of their publication. To do
so, the user must enter the publication date of the article in the system, then the system
will arrange the article in an appropriate section.
In our opinion, there is a lack of methods that we can use for the structurization of scientific
articles. Also, it is necessary to add “By the Results” and “By the methods” methods.
• “By the Results” this method structures the article by its results or conclusions. The user
needs to write down the outcomes or verdicts of the text in the program by himself.
• “By the Methods” this method structures the article by the scientific methods or experi-
ments that are used in the essay.
Considering that most of the articles have a typical IMRAD (Introductions, Methods, Results,
Abstract, Discussion) structure, it seems advisable to build an algorithm that uses data of many
specific articles to create ontological graphs, that can be integrated in specialized educational
environments for young scientists.
45
1.3. Instruments for creation of ontology-based learning environments
A learning environment is a diverse platform where users engage and interact to learn new
skills. While learners can learn in various settings, the term typically refers to a digital alterna-
tive for the traditional classroom. To improve learning efficiency and adaptability, formalized
information resources that provide a high degree of structuring should be used in learning.
An ontological approach could support this. The ontological approach provides a holistic and
systematic approach to the study of various information sources and a specific subject domain,
ensures the conceptualization and taxonomization of terms within the subject area and the exis-
tence of relationships between the terms of different subject areas to ensure multidisciplinarity.
Computer ontologies are one of the effective mechanisms for ensuring a stable digital learning
environment.
In recent years significant progress was made in developing ontologies. In this article, an
“ontology” is a term that means a software or web system that consists of nodes with data. All
ontology nodes are arranged in a specific hierarchical order, often referred to as an ontological
tree or ontological graph. The node from which all branches start is called the root node. The
other nodes are called subsidiaries.
One of the most perspective solution, in our opinion, is ontologies [9]. For example, we can
use hierarchies with multicriteria techniques during the classification of metadata of various
articles. Ontologies aim to capture the domain knowledge in a general way and ensure a
common understanding of the domain.
IsaViz is a virtual environment for viewing and creating RDF models in the view graphs.
IsaViz imports RDF/XML and N-Triples, and exports RDF/XML. Apollo is the program for
modelling knowledge systems. Apollo knowledge system base consists of hierarchically or-
ganized ontologies that can be inherited from other ontologies. SWOOP contains OWL (Web
Ontology Language) validation and offers various. OWL presentation syntax views. At SWOOP,
Ontologies can be compared, edited, and combined. Protégé 3.5 is a knowledge-based ontology
editor that provides a graphical user interface. It ensures better flexibility for metamodelling,
enables the construction of domain ontologies.
1.4. Ontological problems
Nowadays most of the common systems (such as Mendeley, Scopus etc.) provide support
for displaying of data but not for comparison and providing search functions. Also, given
that articles in the same domain have the same indicators, the metadata of the results can be
represented as ontology node attributes and then processed.
Previously, ontological graphs were used to systematize scientific articles [9, 10, 11, 12, 13].
Systematization and structuring in such ontological systems were based on different approaches
such as using of scientific article recommendation system [10], a scientific articles tagging system
[11], machine learning and automatic summarization [9]. None of the proposed ontological
approaches [9, 10, 11, 12] can’t provide a decent level of structurization and systematisation.
We have proposed to use the cognitive IT platform Polyhedron [14] for this aim. The
core of the Polyhedron system consists of advanced and improved functions of the TODOS
IT platform [14] described in previous works. The Polyhedron is a multiagent system that
46
provides transdisciplinary and interactivity in any study [15]. Besides, cognitive IT-platform
Polyhedron contains a different variety of special functions like auditing [15, 16, 17], semantic
web, information systematization and ranking [18] transdisciplinary support [19], internal
search [20], and have all advantages of ontological interface tools [16, 21]. Due to active states
are hyperratio plural partial ordering [19, 22], cognitive IT-platform Polyhedron is an innovative
IT technology of ontological management of knowledge and information resources, regardless
of the standards of their creation.
The proposed solution can be used with other applications in the field of structuration studies
like a virtual educational experiment, provide STEM approach in education [14, 23], using the
technology of augmented reality education [24, 25], educational and scientific environments
[14, 20].
2. Materials and methods
2.1. Ontology creation mechanism
The proposed research is based on approach that was proposed before [26], but it provides
management and structuring of set of the studies, not single. To create ontologies in cognitive
IT-platform Polyhedron, Google Sheets are used to collect and structure the information (see
example in figure 2). The sheets with study report data (structure file and numeric/semantic data
file) are downloaded and saved in .xls format. The data was separated with expert involving and
based on principle that researches of same field have similar input and resulting characteristics.
Same is relevant to field of anaerobic digestion [27, 28] that was taken as example to provide
structuring and processing using proposed method. The files are loaded to editor.stemua.science
to provide graph generation (a part of the cognitive IT platform Polyhedron). After that, the
generation of the graph nodes (in .xls) with its characteristics using structure file is carried out.
The obtained graphs are saved in .xml format and located in the database. The graphs are filled
by semantic and numeric information for ranking or filtering. Ontological nodes are formed
using predicate equations.
2.2. Description of the works were used to demonstrate the structuration
mechanism
To demonstrate the structuring of the educational study reports, a master thesis of the National
University of Food technologies “Development a method for utilization of anaerobic digestion
effluent at LLC Vasylkivska Poultry Farm” (in further – report “A”) and a study report prepared
for the defence of an educational study in the Junior academy of sciences of Ukraine scientific
competition “Development a method for utilization of anaerobic digestion effluent” (in further –
report “B”) are used as input data. Two ontologies have been built, the structure of which dupli-
cates the content of the study reports. All numeric data was separated to provide information
processing and to provide integration between those works. Their titles have used as the parent
node for the ontological graphs. All ontological graphs in the cognitive IT platform Polyhedron
have been linked to each other by the mechanism of internal search. Further scientific studies
will be linked in the graphs by edges (links) to provide better connection.
47
Figure 2: Google sheet with data.
2.3. Ranking tools
Study reports “A” and “B” were also compared with each other by the following criteria: “Short-
term economic perspective”, “Long-term economic prospects” using the ranking tool. For
creation of the ranking ontologies have been used by the module “Alternative” which is described
in previous works [18, 29]. The nodes of the 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.
2.4. Auditing tools
To provide an audit, the “standard” graph (with which the comparison is done) and the “com-
parison” graph (which is compared with the “standard”) have been created. The “standard”
ontology graph contains the data on hypotheses, subjects, objects of study, keywords, and other
parameters, of the studies reports done before. For the “standard” graph, each parameter was
presented in a separate node. The content of this ontological graph “standard” is updated and
supplemented constantly.
The nodes of the “comparison” graph have been presented with the names of the works which
need to be audited with the “standard” graph. The parameters of the work used to audit 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 “standard” graph to provide interaction
between graphs.
48
3. Results
3.1. Model of creation ontology to systemizing
As was noted before, IMRAD is widely used to prepare research and science papers. It is possible
to provide structuration by the usage of IMRAD components as parent nodes. So, the parent
nodes can be represented by Introduction, Methods, Results and Discussion. The discussion
part can’t be easily structured by an ontology. The most contractionary part that it contains
are the analysis and the comparison of the obtained data by the researcher. Specific parts of
IMRAD will be used as branch nodes, and the study will be used as a leaf node. So, the general
structure of the ontology that structures the research data is represented as:
𝑅𝐸𝑃 ∈ {𝐼, 𝑀, 𝑅, 𝑃 } (1)
where 𝑅𝐸𝑃 – set of reports, 𝐼 – sets of Introduction of all study, 𝑀 – set of Methods of all
study, 𝑅 – set of Results of All study, 𝑃 – instruments of processing of the results of set of
studies discussions.
To provide better systematization we have split the introduction into two different parts –
basic metadata and literature review:
𝐼 = ⟨𝐵𝑀 𝐷, 𝐿𝑅⟩ (2)
where 𝐵𝑀 𝐷 – is a set of basic metadata of study, 𝐿𝑅 – a set of Sourses used for Literature
Review.
The basic metadata node of the study is linked with the graph’s leaf nodes that characterized
basic data on the study such as hypothesis, object, subject, practical value, and scientific novelty,
etc. So, the nodes of the report’s basic metadata of the study can be presented as a further
equation:
𝐵𝑀 𝐷 = ⟨𝐻, 𝑂, 𝑆, 𝑃 𝑉, 𝑆𝑁 ⟩ (3)
where 𝐻 – hypothesis or hypotheses of each specific study; 𝑂 – object of the study of each
specific study; 𝑆 – subject of the of each specific study; 𝑃 𝑉 – practical value of each specific
study; 𝑆𝐶 – scientific novelty of each specific study.
The main advantages of using such a structure are that some parts of the introduction
materials and methods and results (measured parameters) of study (reports) can coincide. A
few specific studies that coincide will be linked by nodes (in case of methods and results) or
by classes of data (in case of keywords or scientific novelty) due to using the same subnodes
of the ontology. Let’s represent each work as a set of the Introduction, Methods, Results, and
Processing of the data (Discussion):
𝑅𝐸𝑃𝐼 = ⟨𝐼𝐼 , 𝑀𝐼 , 𝑅𝐼 , 𝑃𝐼 ⟩ (4)
𝑅𝐸𝑃𝐼𝐼 = ⟨𝐼𝐼𝐼 , 𝑀𝐼𝐼 , 𝑅𝐼𝐼 , 𝑃𝐼𝐼 ⟩ (5)
So, these studies can be integrated in a single ontology using IMRAD:
𝑂 = ⟨𝑆𝐼 , 𝑆𝐼𝐼 ⟩ = ⟨𝐼𝐼 , 𝑀𝐼 , 𝑅𝐼 , 𝑃𝐼 , 𝐼𝐼𝐼 , 𝑀𝐼𝐼 , 𝑅𝐼𝐼 , 𝑃𝐼𝐼 ⟩ (6)
49
The same approach will be applied to each element of the IMRAD structure study. Generally,
it can be represented as:
𝑛
∑︁
𝑀 = (𝑅𝐸𝑃𝑖 ) = 𝑀𝐼 (7)
𝑖
where 𝑀𝑖 – every separated scientific method.
In a different study, a different set of methods can be used. However, some of them can
coincide. Thus, set of methods used in two different study may be represented as:
𝑀 = (𝑅𝐸𝑃𝐼 ) = {𝑀𝑎 , 𝑀𝑏 , 𝑀𝑐 , 𝑀𝑑 } (8)
𝑀 = (𝑅𝐸𝑃𝐼 𝐼) = {𝑀𝑏 , 𝑀𝑑 , 𝑀𝑓 } (9)
And, so, 𝑀𝑏 coinciding to both 𝑀𝐼 and 𝑀𝐼𝐼 :
𝑀𝑏 ∈ {𝑀𝐼 , 𝑀𝐼𝐼 } (10)
Therefore, in this case, and 𝑀𝑏 can be used as a parent node that connects two different
studies. The node 𝑀𝑏 itself will contain general theoretic information on it, and node 𝑅𝐸𝑃𝐼
and 𝑅𝐸𝑃𝐼𝐼 will contain information on the specific case of its usage and measured parameters
using it.
Similar mechanism can be provided by using specific ontology tools using metadata. For
example, there will be a hierarchical approach for representing and usage of keywords:
𝐾𝑤(𝐵𝑀 𝐷𝑖 ) = 𝐶𝑙𝑎𝑠𝑠 “𝐾𝑒𝑦𝑤𝑜𝑟𝑑𝑠” 𝑇 𝑦𝑝𝑒 “𝐴𝑟𝑟𝑎𝑦 𝑉 𝑎𝑙𝑢𝑒” {𝐾𝑤𝑎 , 𝐾𝑤𝑏 , 𝐾𝑤𝑐 , 𝐾𝑤𝑑 }, (11)
where 𝐾𝑤(𝐵𝑀 𝐷𝑖 ) – node of the basic metadata that integrates all keywords; 𝐾𝑤𝑖 – specific
keyword.
Also, as was noted in the introduction, the metadata of each work will be used for filtering
the information, and for supporting specific processing functions of the IT solution Polyhedron.
Such specifics mechanisms are AUDIT and RANKING. Metadata can be included in each node.
For the parent node metadata will be used to represent the general information (for example,
essence of the method itself), and the resulting leaf node will contain the specific metadata
related to specific study (such as specific results of the study obtained using set methods M; for
example, metadata: 5.35, and its class: “Ammonium nitrogen content, g/L”). So, metadata, with
the same class, will be processed by using filtering by users request or by ranking using the
ranks of the nodes for specific classes (or their set) based on the user’s request.
So, the proposed approach uses IMRAD to collect and process the data with ontologies. In
this way, the ontologies are constructed not by the specific structure of each work but by the
generally accepted IMRAD structure. The parent node will be a specific area set to which
𝑛
the study belongs (𝐴 = 𝑅𝐸𝑃𝐼𝑖 , where 𝐴 – specific area of set of 𝑅𝐸𝑃 ). The 𝐴 node is
∑︀
𝑖
linked with 𝐼, 𝑀 , 𝑅, 𝑃 nodes (representing IMRAD). Each IMRAD node is linked with the
specific IMRAD type node (such as ammonia determination by Nessler’s method (for methods)
or “chicken manure” or “glycerine” (for subjects)). And each specific IMRAD type node is linked
with leaf nodes of ontology – specific studies where such entities were used.
50
3.2. Structuring several works simultaneously using an ontological graph
To demonstrate the capabilities of the proposed ontological system, scientific works on anaerobic
digestion were chosen. The general view of the resulting graph is shown in figure 3.
Figure 3: General view of the resulting ontological graph.
The root node of the resulting graph is the “Scientific reports” node. In its structure, the
ontological graph is complex because it has additional branches from child nodes. Child nodes
are: “Reports on biotechnology” and “Reports on anaerobic digestion.” From the child node
“Reports on anaerobic digestion” are going the central sub-leaf nodes that reflect the basic
principle of systematization of scientific works: “Results,” “Materials and methods,” “References”.
This basic principle is shown in figure 4. A separate node of Main Metadata was also additionally
created. This node contains the central metadata: object, subject of study; practical significance,
the scientific novelty of study; hypotheses; keywords; abstract, conclusions.
Figure 4: General view of the main systematizing ontological vertices.
51
The entire sequence and principle of filling and maintenance of data by users in the received
ontology are shown in the Workflow diagram (figure 5).
Figure 5: Workflow diagram of filling and maintenance of data by users.
The child nodes of each of these systematizing ontological nodes are the scientific works
themselves. Each data block is in the form of separate attributes of the ontological node. This
solution allows you to use all the information processing tools of the CIT Polyhedron system. In
particular, such tools are both general (for example, filtering) and specialized, such as ranking
and auditing. An example of filtering is shown in figure 6.
3.3. Application of ranking mechanism in the structuring of scientific works
All attributes can be used to rank information using the “Alternative module” described in
previous works [18, 29]. Each nodes attributes is filled with numeric, textual, and/or mixed
types of data. The following attributes are filled with text data: “References”, “Methods for
Quantitative Analysis”, “Materials for researching”, “Thermophilic”, “Chicken manure sub-
strate”, “Spectrophotometer parameters of the experiment”, “The actual rate of reproduction of
”, “Keywords”, “Glassware”, “Reagents”, “Equipment”, “Object ”, “Subject of study”, “The aim of
the study”, “Chicken”. Numeric data contains the following attributes: “Initial pH”, “Methane
content, % Vol.”, “pH of obtained solid product”, “Ammonium nitrogen concentration, mg/L”,
“The concentration of volatile fatty acids (VFA) mg/L”, “The dry matter content, %”, “The ash
content, %”. The attribute “The native moisture content of the substrate” contains mixed-type
52
Figure 6: Example of attribute filter.
data, numeric and textual. An example of an incoming data maintenance panel for ranking is
shown in figure 7.
For example, there may be a case when user wants to arrange work on the pH. The ranking
result is shown in figure 8. Other examples of usage of the Polyhedron IT platform are shown
in table 1.
53
Figure 7: Example of a water panel for data ranking.
Figure 8: Example of ranking results.
3.4. Application of the audit mechanism in structuring scientific works
Users can also use the specialized audit module described in previous works [15, 16, 17, 29]
for all attributes. The graph “Standard” is the ontology itself, containing works that will be
supplemented and expanded. This solution will allow users to automatically check whether
there is a particular work in the database. Also, this solution will allow checking the hypotheses
for compliance with already completed studies. Also, the audit module will allow users to
compare an existing METADATA and attributes that are available in ontology at the same time.
In particular, these attributes are the materials and methods of the results and the list of sources.
Results that do not match the attributes of the “standard” ontology emphasize red. An example
54
Table 1
Examples of Polyhedron IT platform ranking module
Name of fil- Priority Main results (list of ontological nodes)
ter (Ontologi-
cal attributes)
Initial pH Absolute Development of a rational way for utilization of meta-tank waste at JSC
(type of data “Vasylkivska poultry farm”, Titrimetry, Methane tank, Development a method
are numbers) for utilization of methane tank effluent Methods for Quantitative Analysis,
Materials for researching, Abstract
Methane con- Absolute Development a method for utilization of methane tank effluent, Development
tent, % Vol. of a rational way for utilization of meta-tank waste at JSC “Vasylkivska
(type of data poultry farm, Methods for Quantitative Analisis, Materials for researching,
are numbers) Abstract
The ash con- Absolute Development of a rational way for utilization of meta-tank waste at JSC
tent, % “Vasylkivska poultry farm, Development a method for utilization of methane
tank effluent, Methods for Quantitative Analisis, Materials for researching,
Equipment, Reagents, Thermophilic, Chicken manure substrate
The dry mat- Absolute Development a method for utilization of methane tank effluent, Development
ter content, % of a rational way for utilization of meta-tank waste at JSC “Vasylkivska
poultry farm Keywords, Methods for Quantitative, Analysis, Materials for
researching, Equipment, Thermophilic Chicken manure substrate
of an audit fragment is shown in figure 9.
Figure 9: Example of audit results.
55
4. Discussion and conclusions
We have proposed to use IMRAD as the main approach to structure the articles in the form of a
semantic ontology. As a proof of the concept, we have implemented a universal ontological solu-
tion that can provide systematization and structuration of any scientific studies. “Polyhedron” is
not a reference manager like “Endnote”, “Mendeley”, and it is also not a scientometric database,
but in the nearest perspective, it‘s possible to convert our technology into a useful analogue.
But, the advantages and potential scenarios for usage of our solution have been demonstrated by
the example of biogas studies. The potential of using ranking and auditing tools in the obtained
ontological database has been also shown. Numeric and semantic characteristics were separated
from the main text and used to process by specialized algorithms of IT Platform Polyhedron.
For example, users can find studies where specific method was used by both, using the structure
and filtering of studies data. The numeric data of studies are processed by the ranking tool that
assigns ranks to nodes depending on the value of these numeric characteristics. The created
ontology allows to obtain the structured set of studies, separate their characteristics, provide
the possibility to view all of the methods, measured parameters in the view of node and use
them to find the studies where they were used. The detailed comparison of our ontological
solution with most common analogues is presented on the table 2 below.
Table 2
Comparison of “Polyhedron” system with similar analogues
Polyhedron* Mendeley Endnote Scopus Google
Scholar
Automatic extraction of the information Present Present Present Absent Absent
from any added PDFs
Tags, keywords, or search the full text of Present Present Present Present Present
most PDFs functions.
Ability to cite articles in word/pages Absent Present Present Absent Absent
Ability to use numeric data of the articles Present Absent Absent Absent Absent
for ranking
Accentuation of important semantic char- Present Absent Absent Absent Absent
acteristics for management of the wide
range of articles
Ability to compare different articles Present Absent Absent Absent Absent
Visualization of the information Present Absent Absent Absent Absent
Usage of IMRAD approach to sort articles Present Absent Absent Absent Absent
*
proposed approach using CIT “Polyhedron”
As we can see from the table above, our ontological solution has all of the basic functions
of the most common software. Our solution can be used as a bibliographic software and as a
scientometric database. In addition, our solution could provide such functions as ranking based
on specific attributes, ability to compare different articles, visualization of the information in
view of an ontological tree or “ontocubes” and usage of IMRAD approach to sort articles.
56
References
[1] U. Noyer, D. Beckmann, F. Köster, Semantic technologies and metadata systematisation
for evaluating time series in the context of driving experiments, in: 11th International
Protégé Conference, 2009, pp. 1–2. URL: https://protege.stanford.edu/conference/2009/
abstracts/P9-Noyer.pdf.
[2] S. Kumar Basak, A Comparison of Researcher’s Reference Management Software: Refworks,
Mendeley, and EndNote, Journal of Economics and Behavioral Studies 6 (2014) 2220–6140.
URL: https://pdfs.semanticscholar.org/1d41/58017608c841c89794fc9fde70e1c061948e.pdf.
[3] V. Kumar, D. Sharma, Cloud Computing as a Catalyst in STEM Education, International
Journal of Information and Communication Technology Education 13 (2017) 38–51. doi:10.
4018/IJICTE.2017040104.
[4] J. Salem, P. Fehrmann, Bibliographic Management Software: A Focus Group Study of the
Preferences and Practices of Undergraduate Students, Public Services Quarterly 9 (2013)
110–120. doi:10.1080/15228959.2013.785878.
[5] S. Ram, K. John Paul Anbu, The use of bibliographic management software by Indian
library and information science professionals, Reference Services Review 42 (2014) 499–513.
doi:10.1108/RSR-08-2013-0041.
[6] C. Ivey, J. Crum, Choosing the right Management tool, Journal of the Medical Library
Association 106 (2018) 399–403.
[7] A. Butros, S. Taylor, Managing information: evaluating and selecting citation management
software, a look at EndNote, RefWorks, Mendeley and Zotero, Proceedings of the 36th
IAMSLIC Conference: Mar del Plata, Argentina, 17-21 October, 2010 (2011) 53–66. URL:
http://hdl.handle.net/1912/4595.
[8] S. Cuschieri, V. Grech, N. Calleja, WASP (Write a Scientific Paper): The use of bibliographic
management software, Early Human Development 128 (2019) 118–119. doi:10.1016/j.
earlhumdev.2018.09.012.
[9] D. Parveen, A Graph-based Approach for the Summarization of Scientific Articles, Ph.D.
thesis, Ruprecht-Karls-Universität Heidelberg, 2018.
[10] 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.
[11] 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. doi:10.1142/S0218843020500045.
[12] N. Perraudin, Graph-based structures in data science : fundamental limits and applications
to machine learning, Ph.D. thesis, École Polytechnique Fédérale De Lausanne, 2017. URL:
https://infoscience.epfl.ch/record/227982?ln=en.
[13] Y. Poulakakis, K. Vassilakis, M. Kalogiannakis, S. Panagiotakis, Ontological modeling
of educational resources: a proposed implementation for Greek schools, Education and
Information Technologies 22 (2017) 1737–1755. doi:10.1007/s10639-016-9511-z.
[14] Y. B. Shapovalov, V. B. Shapovalov, V. I. Zaselskiy, TODOS as digital science-support
environment to provide STEM-education, CEUR Workshop Proceedings 2433 (2019)
232–245.
57
[15] O. 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.
[16] L. Globa, M. Kovalskyi, O. Stryzhak, Increasing web services discovery relevancy in the
multi-ontological environment, in: A. Wiliński, I. E. Fray, J. Pejaś (Eds.), Soft Computing
in Computer and Information Science, Springer International Publishing, Cham, 2015, pp.
335–344. doi:10.1007/978-3-319-15147-2_28.
[17] L. Globa, S. Sulima, M. Skulysh, S. Dovgyi, O. Stryzhak, Architecture and Operation
Algorithms of Mobile Core Network with Virtualization, in: J. H. Ortiz (Ed.), Mobile
Computing, IntechOpen, 2020, pp. 1–22. doi:10.5772/intechopen.89608.
[18] V. V. Gorborukov, O. Y. Stryzhak, O. V. Franchuk, V. B. Shapovalov, Ontological repre-
sentation of the ranking alternatives task, Mathematical modeling in economics (2018)
49–69.
[19] B. Nicolescu (Ed.), Transdisciplinary: Theory and Practice, Hampton Press, 2008.
[20] V. B. Shapovalov, Y. B. Shapovalov, Z. I. Bilyk, A. I. Atamas, R. A. Tarasenko, V. V. Tron, Cen-
tralized information web-oriented educational environment of Ukraine, CEUR Workshop
Proceedings 2433 (2019) 246–255.
[21] D. Martyniuk, M. Falkenthal, N. Karam, A. Paschke, K. Wild, An analysis of ontological
entities to represent knowledge on quantum computing algorithms and implementations,
CEUR Workshop Proceedings 2836 (2021) 1–9.
[22] R. Volckmann, Transdisciplinarity: Basarab Nicolescu Talks with Russ Vol-
ckmann, Integral Review 4 (2007) 73–90. URL: https://integral-review.org/
transdisciplinarity-basarab-nicolescu-talks-with-russ-volckmann/.
[23] Y. B. Shapovalov, V. B. Shapovalov, F. Andruszkiewicz, N. P. Volkova, Analyzing of main
trends of STEM education in Ukraine using stemua.science statistics, CEUR Workshop
Proceedings 2643 (2020) 448–461.
[24] V. B. Shapovalov, Y. B. Shapovalov, Z. I. Bilyk, A. P. Megalinska, I. O. Muzyka, The Google
Lens analyzing quality: An analysis of the possibility to use in the educational process,
CEUR Workshop Proceedings 2547 (2020) 117–129.
[25] Z. I. Bilyk, Y. B. Shapovalov, V. B. Shapovalov, A. P. Megalinska, F. Andruszkiewicz,
A. Dolhanczuk-Sródka, Assessment of mobile phone applications feasibility on plant
recognition: comparison with Google Lens AR-app, CEUR Workshop Proceedings 2731
(2020) 61–78.
[26] Y. B. Shapovalov, V. B. Shapovalov, R. A. Tarasenko, S. A. Usenko, A. Paschke, A semantic
structuring of educational research using ontologies, CEUR Workshop Proceedings 2879
(2020) 105–123.
[27] Y. Shapovalov, S. Zhadan, G. Bochmann, A. Salyuk, V. Nykyforov, Dry anaerobic digestion
of chicken manure: A review, Applied Sciences 10 (2020) 7825.
[28] 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.
[29] R. A. Tarasenko, V. B. Shapovalov, S. A. Usenko, Y. B. Shapovalov, I. M. Savchenko, Y. Y.
Pashchenko, A. Paschke, Comparison of ontology with non-ontology tools for educational
research, CEUR Workshop Proceedings 2879 (2020) 82–104.
58