Structurization and Processing of the Scientiﬁc Studies in the Form of

Digital Ontologies

Yevhenii Shapovalov

1 a

, Viktor Shapovalov

1 b

, Roman Tarasenko

1 c

, Stanislav Usenko

1 d

Adrian Paschke

2 e

and Iryna Savchenko

1 f

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

Fraunhofer FOKUS (with support of BMBF “Qurator” 03WKDA1F), Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany

Keywords:

Cognitive IT-Platform Polyhedron, Ontology, Ontological Tool, Scientiﬁc Studies, Scientiﬁc Reports,

Learning Environments.

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

used in a speciﬁc research domain. This problem was partially solved by bibliographic management software

and other speciﬁc software. This article aims to develop an approach for structuration and processing sets of

studies using the IT Platform Polyhedron using an ontology-based hierarchical model. The ontological graph

is complex because it has additional branches from child nodes in its structure. The basis of our solution

was IMRAD (Introductions, Methods, Results, Abstract, Discussion structure) 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 separate their characteristics. Thus, the

proposed ontology allows viewing all methods, measured parameters, etc., of the studies in a graph node

structure and using them to ﬁnd the used studies. The usage workﬂows to demonstrate mechanisms of the

system’s usage are presented. The method of merging a few graphs of studies is developed and presented.

1 INTRODUCTION

Usage of information technologies (IT) in various

ﬁelds of research activities and the capability of

software support in science to automatically clas-

sify and structure information, e.g., in publication

data, becomes increasingly important. Nowadays,

vast amounts of research data are available that is not

structured, e.g., publications, presentations, etc. It is

complicated 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 the early stage

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

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

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

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

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

https://orcid.org/0000-0002-0273-9496

of their scientiﬁc career. When preparing papers and

reports, such literature search and analysis problems

(e.g. state of the art) are challenging for every sci-

entist (including youth and school researchers). For

instance, according to Lens.org, the number of arti-

cles on biogas in 2002 was approximately 134, then

in 2014, the number grew to almost 1164, as shown

in (ﬁgure 1).

So, it is relevant to provide a solution that can

simplify processing and information/knowledge ex-

traction in scientiﬁc publications. There are two hy-

potheses in our study. The ﬁrst one is about struc-

turing and digitalizing 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 infor-

mational system. Previously, this goal was partially

achieved using metadata for data processing. In this

paper, we further contribute with a semantic ontology

and a more expressive semantic metadata approach.

362

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

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies.

DOI: 10.5220/0012064400003431

In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology (AET 2021), pages 362-377

ISBN: 978-989-758-662-0

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

Figure 1: Dynamic of the number of papers on biogas.

1.1 Using Metadata to Provide Data

Management in Science

Publications

It is relevant to use metadata about each paper to

support publication data management. This metadata

represents the data about the publication. In this case,

the metadata can represent relevant information about

each speciﬁc publication. Metadata can include, e.g.,

contact information, year of publication, author de-

tails, instrument and protocol information, survey tool

details and much more (Noyer et al., 2009).

For instance, reference management software

maintains a database of articles and creates bibliogra-

phies and reference lists for the written works. This

software simpliﬁes the record of metadata. Several

popular reference management software, for exam-

ple, Refworks, Mendeley, EndNote and Zotero are

used worldwide (Kumar Basak, 2014; Parabhoi et al.,

2017; Salem and Fehrmann, 2013; Ram and Paul

Anbu K., 2014). These managers can save proﬁles,

build a database of citations, save PDF ﬁles, extract

metadata, and import references from library cata-

logues, websites, and other citation managers (Ivey

and Crum, 2018; Butros and Taylor, 2011; Cuschieri

et al., 2019).

However, these systems only use limited meta-

data vocabularies without expressive semantic mod-

els. For instance, such systems do not support meta-

data concepts such as “Results,” “Materials and meth-

ods,” “References”, etc. As a result, all these systems

do not provide a systematic approach, and they are not

entirely semantically structured and not hierarchical.

1.2 Using Metadata Information of

Scientiﬁc for Automatic Literature

Review Processing

There are different types of metadata information that

we can use to structure the articles, for example, by

the relation to source, function, purpose, language,

and publication date.

With using the a “Relation to Source” metadata

the user deﬁnes the type of text that can be included

in a classiﬁcation program. A classiﬁcation composed

of extracts having exact sentences of a source docu-

ment is known as an extractive summary. That is the

simplest type of classiﬁer.

With using a “Function” metadata the user can use

any helpful and relevant information from source doc-

uments, for example, an abstract of a scientiﬁc article

or the reviewers’ opinion on the quality of work.

With using a “Purpose” metadata structures the ar-

ticle’s purpose or main idea. The user needs to write

down the general purpose or sense of the text in the

program by himself.

With using a “Language” metadata, a classiﬁer

can be monolingual or multilingual is used. The

monolingual classiﬁer uses only one language and

produces an output classiﬁcation in the same language

as the input document. In contrast, the multilingual

classiﬁer uses multiple languages and gives an output

classiﬁcation in one of the languages from the input

document.

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

363

With using “Publication date” metadata, it is pos-

sible to arrange the articles by the time of their pub-

lication. To do so, the user must enter the article’s

publication date in the system, and then the system

will arrange the article in an appropriate section.

There is a lack of methods for the structuration of

scientiﬁc articles. Also, it is necessary to add meta-

data “By the Results” (each speciﬁc results that has

been obtained during study) and “By the methods”

methods (each speciﬁc method that has been obtained

during study).

“By the Results” this method structures the article

by its results. It provides possibility to process new

numeric and semantic data that was obtained during

research.

“By the Methods” this metadata is used structure

studies by the scientiﬁc methods and matherials used

in the study.

Considering that most articles have a typical IM-

RAD (Introductions, Methods, Results, Abstract, Dis-

cussion) structure, it seems advisable to build an al-

gorithm that uses data from many speciﬁc articles to

create ontological graphs that can be integrated with

specialized educational institutions environments for

young scientists.

1.3 Instruments for the Creation of

Ontology-Based Learning

Environments

A learning environment is a diverse platform where

users engage and interact with learning new skills.

While learners can learn in various settings, the term

typically refers to a digital alternative to the tradi-

tional 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 informa-

tion sources and a speciﬁc subject domain, ensures the

conceptualization and taxonomization of terms within

the subject area and the existence of relationships be-

tween 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 ar-

ranged 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 promising solutions, in our opin-

ion, are ontologies (Parveen, 2018). For example, we

can use hierarchies with multi-criteria techniques dur-

ing the classiﬁcation of metadata of various articles.

Ontologies aim to capture the domain knowledge gen-

erally and ensure a common understanding of the do-

main.

IsaViz is a virtual environment for viewing

and creating RDF models in the view graphs.

IsaViz imports RDF/XML and N-Triples and ex-

ports RDF/XML. Apollo is the program for modelling

knowledge systems. Apollo’s knowledge system base

consists of hierarchically organized ontologies that

can be inherited from other ontologies. SWOOP con-

tains OWL (Web Ontology Language) validation and

offers various. OWL presentation syntax views. In

SWOOP, Ontologies can be compared, edited, and

combined. Prot

e 3.5 is a knowledge-based ontol-

ogy editor that provides a graphical user interface. It

ensures better ﬂexibility for meta-modelling and en-

ables the construction of domain ontologies.

1.4 Ontological Problems

Nowadays, most standard systems (such as Mendeley,

Scopus etc.) provide support for displaying 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 pro-

cessed.

Previously, ontological graphs were used to sys-

tematize scientiﬁc articles (Parveen, 2018; Amami

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

Poulakakis et al., 2017). Systematization and struc-

turing in such ontological systems were based on dif-

ferent approaches, such as using of scientiﬁc arti-

cle recommendation system (Amami et al., 2017), a

Scientiﬁc Articles Tagging system (Boughareb et al.,

2020), machine learning and automatic summariza-

tion (Parveen, 2018). However, none of the proposed

ontological approaches (Parveen, 2018; Amami et al.,

2017; Boughareb et al., 2020; Perraudin, 2017) can

provide a decent level of structurization and system-

atization.

We have proposed to use the cognitive IT plat-

form Polyhedron (Stryzhak et al., 2019; Velychko

et al., 2017; Strizhak, 2014) for this aim. The core

of the Polyhedron system consists of advanced and

improved functions of the TODOS IT platform de-

scribed in previous works. Moreover, the Polyhedron

is a multi-agent system that provides transdisciplinary

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

364

and interactivity in any study (Stryzhak et al., 2014).

Besides, the cognitive IT platform Polyhedron con-

tains a different variety of special functions like audit-

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

semantic web, information systematization and rank-

ing (Nadutenko et al., 2022), transdisciplinary sup-

port (Nicolescu, 2008; Dovgyi and Stryzhak, 2021),

internal search and have all advantages of ontolog-

ical interface tools (Globa et al., 2015; Popova and

Stryzhak, 2013; Martyniuk et al., 2021) Due to active

states are hyper-ratio plural partial ordering (Nico-

lescu, 2008; Volckmann, 2007), cognitive IT-platform

Polyhedron is an innovative IT technology of ontolog-

ical management of knowledge and information re-

sources, 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 (Slipukhina et al.,

2019), the use of mobile Internet devices (Modlo

et al., 2019), using the technology of augmented real-

ity education (Bilyk et al., 2022; Nechypurenko et al.,

2023), and smart physiological tools (Shapovalov

et al., 2022), distance learning in vocational educa-

tion and training institutions (Kovalchuk et al., 2023),

on-line courses (Vlasenko et al., 2020; Vakaliuk

et al., 2023), educational and scientiﬁc environments

(Tarasenko et al., 2021b; Shapovalov and Shapovalov,

2021), different tools to provide development of ICT

(Information computer technologies) (Modlo et al.,

2018).

2 MATERIALS AND METHODS

2.1 Ontology Creation Mechanism

Systematization of the scientiﬁc studies is provided

using the cognitive IT platform Polyhedron (Shapo-

valov et al., 2021b). Graphs were made in one of

two ways: using a single Google sheet ﬁle (that de-

ﬁnes both structure of the graph and metadata of

each node; or by using of speciﬁc constructor https:

//editor.stemua.science/ or http://work.inhost.com.ua/

that provided a generation of the XML ﬁle of both,

structure and metadata. The ﬁrst Google sheet docu-

ment contained the data on the graph’s structure. The

second sheet of it contained a list of nodes that con-

tains metadata, metadata of this node itself and the

type of metadata (ﬁgure 2). When the ﬁle was ﬁlled,

it was downloaded in .xlsx format and uploaded to

the graph editors. Then, it was transformed into an

XML ﬁle and stored in the graph’s storage. This

method was used to create massive graphs as it simpli-

ﬁes the creation of the graph structure due to no need

to select the location of the nodes visually with the

mouse but locates nodes automatically. Tools https:

//editor.stemua.science/ or http://work.inhost.com.ua/

are graphical editors used to create simple graphs and

test if the structure and metadata of graphs built by

Google sheet’s method are processed well.

As the scientiﬁc works were written with an IM-

RAD structure, IMRAD was used as the core of each

graph. For example, the ﬁeld of anaerobic treatment

of waste was chosen as general studies in this ﬁeld

also used IMRAD (Shapovalov et al., 2021a; Zhadan

et al., 2021; Ivanov et al., 2019). To test using the ap-

proach for both high educational institutions students

and schools’ pupils (in the form of work of Junior

Academy of Sciences of Ukraine), examples of such

studies were used. As the example of master’s study

“Development a method for utilization of anaerobic

digestion efﬂuent at LLC Vasylkivska Poultry Farm”

(study “A”); as an example of school student’s study

“Development a method for utilization of anaerobic

digestion efﬂuent (study “B”). Both studies were rep-

resented in the form of graphs.

To represent ontology graphs, the structure view

in the form tools of view https://manlab.stemua.

science/ and http://work.inhost.com.ua/ were used. In

addition, ontology representer https://editor.stemua.

science/ or http://work.inhost.com.ua/ has a structure

view, table view and speciﬁc ontology prism view.

Metadata of generated graphs were used in ﬁlter-

ing, ranking and audit tools. The simplest toll is ﬁlter-

ing. It provides ﬁltering of graph nodes by the pres-

ence of metadata in the nodes. The audit tool provides

comparing of scientiﬁc studies with a standard graph.

For science, this tool may provide two aims: deﬁne

if such element of study (object, subject, keywords,

etc.) was used previously to do not duplicate existing

study; to provide simple (basic) antiplagiarism.

The ranking tool was used to compare numeric

data in nodes and provide rank using users’ requests.

IT Polyhedron has a speciﬁc interface to input the im-

portance of each metadata category that’s numeric us-

ing a scale from 0 to 10. System process user’s re-

quest by using values inputted by users and numeric

data of nodes. Then it formed the rank of each node

and showed it to the user.

To combine graphs, the ﬁrst graph of scientiﬁc

study that is IMRAD-based was downloaded to http:

//work.inhost.com.ua/ and then its tool “add XML”

was used to add nodes and the link between them of

the second IMRAD-based ontology graph of scien-

tiﬁc study.

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

365

Figure 2: Google sheet with data.

3 RESULTS

3.1 Model of Creation Ontology to

Systemizing

As was noted before, IMRAD is widely used to pre-

pare research and science papers. It is possible to

provide structuration by using IMRAD components

as parent nodes. So, the parent nodes can be repre-

sented by Introduction, Methods, Results and Discus-

sion. An ontology cannot efﬁciently further structure

the discussion part. Important part that it contains is

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 a set of studies discussions.

To provide better systematization, we have split

the introduction into two different parts – basic meta-

data and literature review:

I = ⟨BMD, LR⟩ (2)

where BMD – is a set of basic metadata of study, LR –

a set of Sources used for Literature Review.

The primary metadata node of the study is linked

with the graph’s leaf nodes that characterize primary

data on the study, such as hypothesis, object, subject,

practical value, scientiﬁc novelty, etc. So, the nodes

of the report’s basic metadata of the study can be pre-

sented as a further equation:

BMD = ⟨H, O, S, PV, SN⟩ (3)

where H – hypothesis or hypotheses of each speciﬁc

study; O – object of the study of each speciﬁc study;

S – the subject of each speciﬁc study; PV – practical

value of each speciﬁc study; SC – the scientiﬁc nov-

elty 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 the

study (reports) can coincide. A few speciﬁc stud-

ies 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

sub-nodes of the ontology. The representation of each

work as a set of the Introduction, Methods, Results,

and Processing of the data (Discussion):

REP

= ⟨I

, M

, R

, P

⟩ (4)

REP

= ⟨I

, M

, R

, P

⟩ (5)

So, these studies can be integrated into a single

ontology using IMRAD:

O = ⟨S

, S

⟩ = ⟨I

, M

, R

, P

, I

, M

, R

, P

⟩ (6)

The same approach will be applied to each ele-

ment of the IMRAD structure study. Generally, it can

be represented as:

M = (REP

) =

∑

(7)

where M

– every separated scientiﬁc method.

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

366

In a different study, a different set of methods can

be used. However, some of them can coincide. Thus,

the set of methods used in two different studies may

be represented as:

M = (REP

) = M

, M

(8)

M = (REP

I) = M

, M

(9)

And, so, M

coinciding with both M

and M

∈ M

, M

(10)

Therefore, in this case, and M

can be used as a

parent node that connects two different studies. The

node M

itself will contain general theoretic informa-

tion on it, and node REP

and REP

will contain in-

formation on the speciﬁc case of its usage and mea-

sured parameters using it.

Similar mechanism can be provided by using spe-

ciﬁc ontology tools using metadata. For example,

there will be a hierarchical approach for representing

and usage of keywords:

Kw(BMD

) = Kw

, Kw

, (11)

where Kw(BMD

) – node of the basic metadata that

integrates all keywords; Kw

– speciﬁc keyword.

Also, as was noted in the introduction, the meta-

data of each work will be used for ﬁltering the infor-

mation, and for supporting speciﬁc processing func-

tions 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 infor-

mation (for example, the essence of the method it-

self), and the resulting leaf node will contain the spe-

ciﬁc metadata related to a speciﬁc study (such as spe-

ciﬁc results of the study obtained using set methods

M; for example, metadata: 5,35, and its class: “Am-

monium 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 struc-

ture of each work but by the generally accepted IM-

RAD structure. The parent node will be a speciﬁc area

set to which the study belongs (A =

∑

inREP

where

A – speciﬁc area of the set of REP). The A node is

linked with I, M, R, P nodes (representing IMRAD).

Each IMRAD node is linked with the speciﬁc IM-

RAD type node (such as ammonia determination by

Nessler’s method (for methods) or “chicken manure”

or “glycerine” (for subjects)). Moreover each speciﬁc

IMRAD type node is linked with leaf nodes of ontol-

ogy – speciﬁc studies where such entities were used.

3.2 Structuring of the Set of Studies in

the Form of Ontology

To demonstrate the capabilities of the proposed onto-

logical system, scientiﬁc works on anaerobic diges-

tion were chosen. The general view of the resulting

graph is shown in ﬁgure 3.

The root node of the resulting graph is the “Sci-

entiﬁc reports” node. The ontological graph is com-

plex because it has additional branches from child

nodes in its structure. Child nodes are: “Reports on

biotechnology” and “Reports on anaerobic digestion.”

From the child node, “Reports on anaerobic diges-

tion” are going to the central sub-leaf nodes that re-

ﬂect the basic principle of systematization of scien-

tiﬁc works: “Results,” “Materials and methods,” and

“References”. This basic principle is shown in ﬁg-

ure 4. A separate node of Main Metadata was also

additionally created. This node contains the central

metadata: object, subject of study; practical signif-

icance, the scientiﬁc novelty of study; hypotheses;

keywords; abstract, conclusions.

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).

The child nodes of these systematizing ontologi-

cal nodes are the scientiﬁc works themselves. Each

data block is in the form of separate attributes of the

ontological node. This solution allows using all the

information processing tools of the CIT Polyhedron

system. In particular, such tools are general (for ex-

ample, ﬁltering) and specialized, such as ranking and

auditing. An example of ﬁltering is shown in ﬁgure 6.

An ontology prism is a speciﬁc form of ontology

graph in cognitive IT Polyhedron. It provides the pos-

sibility to use nodes and their relation to the form of

a prism. The most helpful form can represent the

relevel of IMRAD and provide a high level of visu-

alization of its sub-nodes. An example of an ontology

cube representation of a graph is shown in ﬁgure 7.

3.3 Application of Ranking Mechanism

in the Structuring of Scientiﬁc

Works

All attributes can rank information using the “Alterna-

tive module” described in previous works (Nadutenko

et al., 2022; Tarasenko et al., 2021a). The attributes

of Each node are ﬁlled with numeric, textual, and

mixed types of data. The following attributes are

ﬁlled with text data: “References”, “Methods for

Quantitative Analysis”, “Materials for researching”,

“Thermophilic”, “Chicken manure substrate”, “Spec-

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

367

Figure 3: General view of the resulting ontological graph.

Figure 4: General view of the primary systematizing ontological vertices.

trophotometer parameters of the experiment”, “The

actual rate of reproduction of ”, “Keywords”, “Glass-

ware”, “Reagents”, “Equipment”, “Object ”, “Sub-

ject of study”, “The aim of the study”, “Chicken”.

Numeric data contains the following attributes: “Ini-

tial pH”, “Methane content, % Vol.”, “pH of ob-

tained solid product”, “Ammonium nitrogen concen-

tration, mg/L”, “The concentration of volatile fatty

acids (VFA) mg/L”, “The dry matter content, %”,

“The ash content, %”. The attribute “The native mois-

ture content of the substrate” contains mixed-type

data, numeric and textual. An example of incoming

data maintenance panel for ranking is shown in ﬁg-

ure 8.

For example, there may be a case when the user

wants to arrange work on the pH. The ranking result

is shown in ﬁgure 9. Other examples of usage of the

Polyhedron IT platform are shown in table 1.

3.4 Application of the Audit Mechanism

in Structuring Scientiﬁc Works

Users can also use the specialized audit module de-

scribed in previous works (Stryzhak et al., 2014;

Globa et al., 2015, 2019; Tarasenko et al., 2021a)

for all attributes. The graph “Standard” is the ontol-

ogy itself, containing works that will be supplemented

and expanded. This solution will allow users to au-

tomatically check whether there is a particular work

in the database. Also, this solution will allow check-

ing the hypotheses for compliance with already com-

pleted studies. Also, the audit module will allow users

to compare an existing METADATA and attributes

available in the ontology at the same time. In par-

ticular, these attributes are the materials and methods

of the results and the list of sources. Results that do

not match the “standard” ontology attributes are em-

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368

Figure 5: Workﬂow diagram of ﬁlling and maintenance of data by users.

Figure 6: Example of attribute ﬁlter.

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

369

Figure 7: Ontology prism view of IMRAD-based graph of scientiﬁc studies.

Figure 8: An example of incoming data maintenance panel for ranking.

phasized in red. An example of an audit fragment is

shown in ﬁgure 10.

3.5 Interoperability of Ontology Graphs

on Students’ Scientiﬁc Studies

To make an effective personalized system, it is essen-

tial to provide interoperability between similar con-

tent of previous studies to allow users to create unique

resources that will be used as the base of knowledge

or literature review of the current researcher. So, in-

teroperability is required. Moreover, using an IM-

RAD structure allows merging graphs because of the

similar parent’s nodes.

So, each student can use the developments made

earlier. In addition, students can download a few ex-

isting ontology graphs from open data located on on-

tology.stemua.science and provide merging using MS

Excel tool or speciﬁcally developed tools.

To solve this task, graphs-blanks (standards) are

relevant to use. The standard consists of broad used

categories for the ﬁeld that will be ﬁlled by re-

searchers who conduct studies. The general view of

such graph-standard is presented in ﬁgure 11.

The researchers could input the speciﬁc data used

during the research. Different researchers would have

their speciﬁc graphs, but they were built by the same

structure using IMRAD standard. Such graphs can be

used to combine a single graph. Thus, the interop-

erability of different studies is provided. Therefore,

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

370

Figure 9: Example of ranking results.

Figure 10: Example of audit results.

such graphs could contain more data on speciﬁc ﬁelds

in the future.

As described in Section 3.1, the graph’s combin-

ing will provide stability of IMRAD structure and the

addition of sub nodes belonging to a speciﬁc study

and the second to second. The two examples of ﬁlled

studies to merge are presented in ﬁgure 12.

For example, the study “Anaerobic treatment un-

der low moisture content” has a node “Biogas” re-

lated to node “Keywords”. The same node also be-

longs to the second study, “Recirculation of the liquid

phase of efﬂuent during anaerobic digestion”. After

combining, the edge “Biogas” will have two subn-

odes “Anaerobic treatment under low moisture con-

tent” and “Recirculation of the liquid phase of efﬂuent

during anaerobic digestion” which provides belong-

ing the keyword “Biogas” to both studies.

As for some other nodes that do not belong to both

studies, they will also be represented in a combined

graph, but they will contain only a single study to

which it belongs. For example, such a case will be

for nodes “Inhibition”, “Ammonia”, “Recycling”, and

“Water consumption”. A general view of the result of

combining is presented in ﬁgure 13.

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

371

(a) (b)

Figure 11: The structure of the ghaph, (a) The function of adding to the graph another graph (combining), (b).

Table 1: Examples of Polyhedron IT Platform Ranking Module.

Name of ﬁlter (On-

tological attributes)

Priority Main results (list of ontological nodes)

Initial pH (type of

data are numbers)

Absolute Development of a rational way for utilization of meta-tank waste at JSC “Va-

sylkivska poultry farm”, Titrimetry, Methane tank, Development a method for

utilization of methane tank efﬂuent Methods for Quantitative Analysis, Materi-

als for researching, Abstract

Methane content, %

Vol. (type of data

are numbers)

Absolute Development of a method for utilization of methane tank efﬂuent, Development

of a rational way for utilization of meta-tank waste at JSC “Vasylkivska poultry

farm, Methods for Quantitative Analisis, Materials for researching, Abstract

The ash content, % Absolute Development of a rational way for utilization of meta-tank waste at JSC “Va-

sylkivska poultry farm, Development of a method for utilization of methane tank

efﬂuent, Methods for Quantitative Analisis, Materials for researching, Equip-

ment, Reagents, Thermophilic, Chicken manure substrate

The dry matter con-

tent, %

Absolute Development of a method for utilization of methane tank efﬂuent, Development

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

4 DISCUSSION AND

CONCLUSIONS

4.1 Cases of Usage

Generally, it is possible to use such an approach

in two cases “Personalized science study graph cre-

ation” and “Combining using Script editor”. The ﬁrst

case foresees a simple scientiﬁc process, but the re-

sults of such a study and its description are provided

by the creation ontology of such work using the graph

template based on IMRAD. So, all researchers will

prepare the description of their work using similar for-

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

372

(a)

(b)

Figure 12: Example of ontology “Anaerobic treatment under low moisture content”, (a) Example of ontology “Recirculation

of the liquid phase of efﬂuent during anaerobic digestion” (b).

malization. Furthermore, it provides the possibility to

implement the “Combining using Script editor” use

case. This use case is an easy way for the researcher

to systemize all studies in his ﬁeld that were done be-

fore. After combining, the obtained graph is merged

as it was created from graphs with the same formal-

ization. This graph is ready to use systemized litera-

ture review of the study for the scientist who provided

it. Tools of Polyhedron will provide data processing

of merged graph by using ranking, ﬁltering and audit.

4.2 Role of Proposed System Among

Existing Systems

Using of IMRAD as the primary approach to structure

the articles in the form of a semantic ontology is pro-

posed. The implementation of a universal ontological

solution that can provide systematization and struc-

turation of any scientiﬁc studies as proof of the con-

cept is provided. The advantages and potential sce-

narios for using our solution have been demonstrated

by the example of biogas studies. The potential of

using ranking and auditing tools in the obtained on-

tological database has also been 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 a speciﬁc method was used by both, us-

ing the structure and ﬁltering of studies data. The nu-

meric 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, provides the possibility to view

Structurization and Processing of the Scientiﬁc Studies in the Form of Digital Ontologies

373

Figure 13: Combined graph of two studies.

Figure 14: Use cases of the proposed approach.

all of the methods, measured parameters in the view

of node and use them to ﬁnd the studies where they

were used. A detailed comparison of our ontologi-

cal solution with the most common analogues is pre-

sented below.

As we can see from the table 2, proposed ontolog-

ical solution has the essential functions of the most

common software. Proposed solution can be used as

bibliographic software and a scientometric database.

In addition, our solution could provide such functions

as ranking based on speciﬁc attributes, comparing dif-

ferent articles, visualizing the information given an

ontological tree or ontology-based cube, and using the

IMRAD approach to sort articles.

Mathematical interpretation of proposed model is

based on that IMRAD foresees those papers consist

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

374

Table 2: Comparison of “Polyhedron” system with similar analogues.

Personalized

science study

graph creation

Combining

graphs using

Script editor

Mendeley Endnote Scopus

Google

Scholar

Automatic extraction of the informa-

tion from any added PDFs

Present Present Present Present Absent Absent

Tags, keywords, or search the full text

of most PDFs functions.

Present Present Present Present Present Present

Ability to cite articles in word/pages Absent Absent Present Present Absent Absent

Count citing Possible Present Absent Absent Present Present

Ability to use numeric data of the arti-

cles for ranking

Present Present Absent Absent Absent Absent

Accentuation of important semantic

characteristics for management of the

wide range of articles

Present Present Absent Absent Absent Absent

Ability to compare different articles Possible Present Absent Absent Absent Absent

Visualization of the information Present Present Absent Absent Absent Absent

Usage of IMRAD approach to sort ar-

ticles

Present Present Absent Absent Absent Absent

*proposed approach using CIT “ Polyhedron”

for, Introduction, Methods, Results and Discussion.

In form of detail systems, it is impossible to provide

Discussion, but the similar results will be provided

by data Processing. In turn, the formed graph of IM-

RAD that formed subject area is linked with speciﬁc

to each study parts of each element of IMRAD (spe-

ciﬁc method or result). Therefore, the graph that de-

scribes few studies will consist from set of speciﬁc to

each study parts of each element of IMRAD.

Such same formalization for different studies pro-

vides possibility to use speciﬁc features of ontologies.

One of the most important is interoperability that is

possible by merging few graphs of studies that has

similar structure into single one. This feature provides

possibilities to make digital ontology-based libraries

of studies.

4.3 Further Research

The proposed approach may be much developed. The

proposed approach foresees using Google Sheets and

then transferring it into XML. However, it seems pos-

sible to simply such a process by generating ontolo-

gies exetly from Google Sheets and providing real-life

synchronization.

Also, as the proposed approach is based on spe-

ciﬁc studies, it seems relevant and possible to provide

scientometry. CIT “Polyhedron” is not a reference

manager like “Endnote”, “Mendeley”, and it is also

not a scientometric database, but in the nearest per-

spective, it is possible to convert the proposed tech-

nology into a functional analogue.

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