Mathematical Interpretation of Educational Student’s and Scientiﬁc

Studies in Form of Digital Ontologies

Viktor B. Shapovalov

and Yevhenii B. Shapovalov

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

Keywords:

Ontology, IMRAD, Structuration, Scientiﬁc Studies, Biogas.

Abstract:

Because of the problem of the large amount of scientiﬁc data generated, it is relevant to develop structuring

and processing methods. Using ontology graphs is the modern perspective way of their representation. Con-

sidering that most studies are written based on IMRAD, it was used to provide integration of different studies

at a single structure and provide structuration at all. The different ways to create integrated ontology using IM-

RAD are described. To get the necessary level of abstraction, IMRAD elements as part of a set of the speciﬁc

studies were decomposed as levels of abstraction from L1 (general node that describes a branch of science)

to L5 (speciﬁc papers with detailed data) depending on the abstraction. The content of each node in the form

of metadata and its further processing is described. The particular way of using proposed modes has been

described in the example of describing studies in biogas production. The mathematical model of the proposed

ontology is developed and presented. It is shown that a set of corteges describes IMRAD representation of the

scientiﬁc studies in the form of ontologies.

1 INTRODUCTION

The data nowadays is generated with colossal inten-

sity. Due to this, Big Data processing is a trend (Globa

et al., 2019; Stryzhak et al., 2021). Processing a con-

siderable amount of data in real life is complicated

by the high gain of publishing scientiﬁc studies. In

general, it seems like an exponentially growing of the

publications. According to lens.org, in 1900, only

532 M of scientiﬁc papers were published, but their

amount in 2015 was near 10 B (ﬁgure 1).

Considering the development of STEM, studies

are provided not only by experienced scientists by

youth. Such a considerable number of studies gen-

erated complicated tasks to process such data. One of

the problems of low spreading and usage (in the ex-

ample of Ukraine (Hrynevych et al., 2021; Martyniuk

et al., 2021; Shapovalov et al., 2020b; Stryzhak et al.,

2017)) may be related to difﬁculties with the process-

ing of science.

Now, scientiﬁc studies are published in different

forms of report, such as articles, conference proceed-

ings, books, etc. However, its process is complicated

due to studies are low-structured. Sure, they are all

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

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

built by a similar structure named IMRAD (Oriokot

et al., 2011; Pardede, 2012). It envisages require-

ments for the paper to consist of some generalized

Introduction, describing used Materials and Methods,

naming the Results of the study and the Discussion by

comparing with other scientiﬁc materials or providing

use cases. However, it seems not enough. Here just

some examples of problems due to it:

• it is hard to start the researcher carrier due to com-

plicated process of understanding of the methods

and equipment that need to be used in speciﬁc

ﬁelds of study;

• it is hard for youth scientists to understand main

parameters that have measured to provide study

analysis;

• for expired scientists, it is hard to analyze and col-

lect data of new studies.

These are only very few cases that are a problem

due to high amount of data of scientiﬁc studies. How-

ever, these cases are makes relevant to develop new

methods to provide better structuration and data pro-

cessing of scientiﬁc studies.

Sure, there are few solutions for this problem that

provides automated science data processing (Klampﬂ

et al., 2014; Portenoy and West, 2020; Gorashy

and Salim, 2014; Shakeel et al., 2018; Paschke and

578

Shapovalov, V. and Shapovalov, Y.

Mathematical Interpretation of Educational Studentâ

Zs and Scientiﬁc Studies in Form of Digital Ontologies.

DOI: 10.5220/0012066200003431

In Proceedings of the 2nd Myroslav I. Zhaldak Symposium on Advances in Educational Technology (AET 2021), pages 578-587

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 published papers according to Lens.org web service.

Sch

afermeier, 2018), but it seems that they do not take

to account IMRAD. One of the appropriate methods

to solve the problems is ontology taxonomies (Globa

et al., 2015; Mintser et al., 2018; Stryzhak et al.,

2018; Sch

afermeier et al., 2021) with semantic tech-

nologies (Alnemr et al., 2010). Also, ontology tax-

onomies have a lot of advantages, such as the possi-

bility to combine with other types of materials (Gru-

ber et al., 2020), including interactive and web-based

courses (Bovtruk et al., 2020; Slipukhina et al., 2018),

other information technologies (Markova et al., 2019;

Modlo and Semerikov, 2018) and GIS GIS (Stryzhak

et al., 2019). This research aims to develop a model

that can structure the set of the studies using IMRAD.

Previously, it was proposed to provide support us-

ing ontologies for single speciﬁc study, but not to cre-

ate glossaries and structured sets of data. To pro-

vide it tools Open provenance, Ontologyt and EXPO

(da Cruz et al., 2012) were developed. Another on-

tology solution in the ﬁeld of science is MoKi that

provides creation of wiki-based information scientiﬁc

sources (Dragoni et al., 2014; Ghidini et al., 2012).

There some speciﬁc ontology tools such as Gene on-

tology (Smith, 2008) or Centralized educational en-

vironment (Stryzhak et al., 2021). However, creation

of ontology to structure the set of the studies seems

relevant due lack of approaches to provide it.

2 METHODS OF THE RESEARCH

In the paper, the ontology model has developed us-

ing the main principles of Graph Theory, Set Theory,

and a Theory of Abstraction (Giunchiglia and Walsh,

1992). The graph was modelled using a simple hier-

archical algorithm that foresees using only nodes and

links. So, such a model further may be updated using

the more comprehensive graph building tools such as

weight coefﬁcients. However, without simple mod-

elling, providing it will not be possible. To provide

structuration generally accepted structuring method

IMRAD has been proposed and used.

To model data processing was developed taking

to account the processing possibilities of the Polyhe-

dron system due it has some advantages compare well

known Prot

e (The Board of Trustees of the Leland

Stanford Junior University, 2020; Ameen et al., 2012)

and OWL tools (Sinha and Couderc, 2012; Soldatova

and King, 2006). Furthermore, the features of cog-

nitive IT-platform tools Filtering, Audit, and Rank-

ing to provide decision-making (Stryzhak et al., 2021;

Shapovalov et al., 2019a,b) were described in equi-

tations to describe the data processing in the ontology

model.

3 RESULTS AND DISCUSSION

3.1 Using IMRAD to Provide Structure

As was noted before, IMRAD is used to prepare sci-

ence papers. So, to provide structuration, it is possi-

ble to use parent nodes that represent IMRAD compo-

nents. IMRAD – Introduction, Methods, Results, and

Discussion. The discussion part can’t be structured by

Mathematical Interpretation of Educational Studentâ

Zs and Scientiﬁc Studies in Form of Digital Ontologies

579

ontology because it contains the obtained data analy-

sis and comparison. That is why discussion will be

represented as the processing of the results.

(D ∈ S) =⇒ (P ∈ S) (1)

where S – study (or set of studies), D – discussion of

studies’ results, P – processing of the results of a set

of studies.

Approximate, ontology can be devoted to a spe-

ciﬁc ﬁeld of science or integrate different ﬁelds. De-

pending on it, the ontology will have 5 or 4 abstract

levels of deep. In the case of general ontology, the

parent node will be “Scientiﬁc studies”, and its sub-

sidiary nodes will name a speciﬁc ﬁeld. In the case

of a speciﬁc ontology, the parent node will name a

speciﬁc ﬁeld. Then it links with elements of IMRAD

structure. Each element of IMRAD has its speciﬁc

representation, and it’s in turn linked with more spe-

ciﬁc for the study describing the element of IMRAD.

And the leaf node will be a set of speciﬁc studies be-

longing to the ﬁeld. Let’s name each level with L

symbols taking to account position in the hierarchy:

L1 – General name of parent’s node “Scientiﬁc stud-

ies”,

L2 – Name of ﬁeld of the study,

L3 – Part of IMRAD,

L4 – Speciﬁc representation of IMRAD (speciﬁc

method, used materials, speciﬁc type of the re-

sults),

L5 – Speciﬁc study where were used speciﬁc repre-

sentations of IMRAD L4.

Therefore, the hierarchy in a speciﬁc study will

have a form of {L2, L3, L4, L5} or the general ones

will have a form of {L1, L2, L3, L4, L5}. Interoper-

ability of the L2 nodes of two different graphs may be

provided by using the graph constructor. It provides

the possibility to merge graphs in two ways. The ﬁrst

foresees that graphs will be constructed as a general

graph in the form of {L1, L2, L3, L4, L5} and with

the same name of L1. And the second is to create L1

in the constructor and add there two speciﬁc graphs in

the form of {L2, L3, L4, L5}. Schematic representa-

tion of the general ontology is shown in ﬁgure 2, and

taxonomy of the speciﬁc ﬁeld is shown in ﬁgure 3.

An alternative and a more humanly more human-

readable way to provide abstraction are to revert this

model and begin with L5 and end with L1. In this

case, ontology will have structure form {L5, L4, L3,

L2, L1}. The graph based on the abstraction that be-

gins from speciﬁc studies L1 and ends by ﬁeld of the

research is shown in ﬁgure 4.

However, the main disadvantage of such a graph

is evident and is the consequences of the structure:

Figure 2: The taxonomy of the general science report ontol-

ogy, where LR1, LR2, M1, R1, R2 – are abstract classes of

literature review (LR), Methods and results of object.

Figure 3: The taxonomy of the speciﬁc ﬁeld science report

ontology.

the leaf node SR (“Scientiﬁc study”) will be not very

useful for users. Anyway, this type of graph may be

built as {L5, L4, L3} and in this case, it will be used to

evaluate the speciﬁc report, for example, during quali-

fying work evaluation (PhD or Master’s study). It will

show abstract classes of each speciﬁc part of IMRAD

for each speciﬁc study and can provide an evaluation

of the set of methods and results that the researcher

obtained. Anyway, in this research, we’ll use the ﬁrst

way to provide hierarchies in the form of {L2, L3, L4,

L5}, and {L1, L2, L3, L4, L5}.

As it can be seen, the general science report ontol-

ogy is signiﬁcantly more complicated due to links be-

tween L1 and L2 levels, and also, there will be some

problems with a vast amount of methods, results, etc.

that can be not necessary to the user that looking for

information on the speciﬁc ﬁeld. Also, it will be much

harder to create such type of graphs due it will have

two levels of links “one to many” (see ﬁgure 2, links

between L2 and L3 level and links between L4 and

L5 levels) compare to only one in case of speciﬁc on-

tology (see ﬁgure 3, only links between L4 and L5

levels). It may be unreasonable to create a compli-

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

580

Figure 4: The graph based on the abstraction that begins

from speciﬁc studies L1 and ends by ﬁeld of the research.

cated graph. Therefore, it seems relevant to provide

both types of hierarchies. To provide it, the ontologies

should be created in speciﬁc ﬁelds and then merged,

as noted before.

In this case, speciﬁc parts of IMRAD will be used

as subsidiaries nodes in the ﬁeld of the study, and spe-

ciﬁc studies will be used as leaf nodes. So, the general

structure of such ontology may be represented as:

{I, M, R,P} ∈ REP (2)

where I – sets of Introduction of all studies, M – set

of Materials and Methods an of all studies, R – set of

Results of all studies, P – processing of the results of

a set of studies; replaces discussion; REP – report (or

set of report).

To provide better systematization and we have

split the introduction into two different parts due to

their speciﬁc – basic metadata and literature review;

it is possible to represent the introduction as further:

I = ⟨BMD, LR⟩ (3)

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

set of Sources used for Literature Review.

Basic metadata of the study node linked with

graph nodes that characterized the essential data on

the study, such as hypothesis, object, subject, practi-

cal value, and scientiﬁc novelty. And so, a node of

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

sented as a further equation:

BMD = ⟨H

, O

, S

, PV

, S

⟩ (4)

where H – hypothesis or hypotheses of each speciﬁc

study; O – object of the study; S – the subject of

each speciﬁc study; PV – practical value of each spe-

ciﬁc study; SC – the scientiﬁc novelty of each speciﬁc

study.

Each work of the set of the Introductions, Meth-

ods, Results, and Processing of the data (Discussion).

Then each work will be represented as the future:

= ⟨I

, M

, R

, P

⟩ (5)

= ⟨I

, M

, R

, P

⟩ (6)

So, these articles can be integrated into a single

ontology using IMRAD:

⟨S

, S

⟩ = ⟨I

, M

, R

, P

, I

, M

, R

, P

⟩ (7)

3.2 Using Taxonomy Nodes as Structure

of Science Data

The main advantages of using such structures are that

some parts of the introduction (for example, key-

word), materials and methods and results elements

(entities and measured parameters) of studies/report

in the same ﬁeld can coincide and, in this case, such

coinciding sub-nodes will be used as links for them

and provide their interoperability. The proposed ap-

proach uses IMRAD to collect and process the data

with ontologies. In this way, the ontologies are con-

structed not by the speciﬁc structure of each work but

by the generally accepted IMRAD structure. The par-

ent node will be a speciﬁc area to which a set of the

studies belongs to (L2 =

∑

, where L2 – speciﬁc

area and RS – set of the represented studies). The L2

node is linked with I, M, R, P nodes (representing IM-

RAD). Each IMRAD node is linked with a speciﬁc

node (such as ammonia determination by Nessler’s

method (for methods) or “chicken manure” or “glyc-

erine” (for subjects)) that belongs to such types. And

each speciﬁc IMRAD type is linked with leaf nodes of

ontology – speciﬁc studies where such entities were

used.

In this case, a few studies/report (REP1, REP2,

and REP3 that belong to L5) will be integrated with

some of the methods or results (M1, R1, R2 that be-

long to L4). So, the L4 level will be used to provide

the structuration of the studies (L5). The user can use

it in both ways: to ﬁnd which method, result, etc.,

that belong to L4 were used in a speciﬁc report that

belongs to L5; and deﬁne in which studies belong to

L5 speciﬁc method, result, etc. that belong to L4 were

used.

The same approach will be provided for each ele-

ment of the structure. General can be represented as:

L4(M) =

∑

(8)

where M

– every separated scientiﬁc method.

Case of coinciding of the methods may be repre-

sented as single mortises of methods of each study:

= {M

, M

} (9)

= {M

, M

} (10)

Therefore, in this case, M

can be used as a parent

node that connects two different studies. The node

Mathematical Interpretation of Educational Studentâ

Zs and Scientiﬁc Studies in Form of Digital Ontologies

581

itself will contain general theoretic information on

it, and node S

and S

will contain information on

the speciﬁc case of its usage and measured parameters

using it.

Also, for example, there will be a hierarchical way

of representing and using the keywords:

(BMD

) = Kw

, Kw

(11)

where K

(BMD

) – node of the basic metadata that

integrates all keywords; Kw

– speciﬁc keyword of

the speciﬁc research.

In this case, some of the studies, same as for the

methods, Kw

will be elements of two different stud-

ies (Kw

, Kw

∈ S

, S

). This will be useful, espe-

cially for students and young scientists looking to ﬁnd

methods (M

) and parameters that can be used in spe-

ciﬁc ﬁelds and their usage in practice. Also, this way

provides a list of the parameters and methods used in

speciﬁc ﬁelds.

3.3 Metadata Processing

The metadata of each work will be used for pro-

cessing the data. It may be included for each node.

For example, metadata of L4 nodes will represent the

general information (for example, the essence of the

method itself), and the resulting leaf nodes will con-

tain the speciﬁc metadata related to a speciﬁc study

(such as speciﬁc results of the study obtained using

set methods M; for example, metadata: “5,35”, and

it’s class: “Ammonium nitrogen content, g/l). And

so, metadata with the same class will be processed by

ﬁltering by users’ request or by ranking by providing

the rank of nodes by speciﬁc class (or their set) based

on the user’s request. So, each node located on each

level E

contains metadata with the abstract level that

corresponds to several levels; for level 1st – it will be

the most abstract metadata, and for 5th – it will be the

most speciﬁc.

As can be seen, all data in levels L1-L4 contains

generalized metadata and wouldn’t be used to process

speciﬁc study, but just used to get generalized abstract

information on entities used in speciﬁc ﬁelds. Only

the L5 level contains metadata related to a speciﬁc

study and will be used for further processing.

3.4 Using Metadata to Provide Data

Processing

Speciﬁc mechanisms “Filtering”, “AUDIT” and

“RANK” of cognitive IT solution Polyhedron are

used to provide processing of the information. It will

be used for the case when different studies will have

the same Class and Type of information, but different

values:

{Class : C1; Type : Number;Value : V 1} ∈ REP1 (12)

{Class : C1; Type : Number;Value : V 2} ∈ REP2 (13)

{Class : C1; Type : Number;Value : V 3} ∈ REP3 (14)

And the values V

can be equal or not equal.

Anyway “Filtering”, “AUDIT” and “RANKING” can

be used to process the data. Filtering can be described

by function if:

If (V

min

< V < V

max

) then (display nodes with such

If (V = V

set

) then (display nodes with such V)

where Vmin, Vmax, Vset are maximum, minimum,

and given (set) values, respectively, that inputted by

the user.

The function of AUDIT can also be described as a

function if:

If (V

= V

set

) than (mark red such V

); for each V

The ranking is much more complicated and can be

described as:

RANK

abs(i)

∑

(OR

× IMP

max

) (15)

where RANK

abs(i)

– ranking rank in absolute value for

i’s node OR

– orientation maximum or minimum for

metadata of i’s object (can be +1 or -1); IMP

– im-

portance coefﬁcient for metadata of i’s object; V

–

the value of metadata of i’s object; V

max

– maximum

value of the set of metadata.

RANK

(abs(i)

RANK

max

(16)

where RANK

– the relative value of the rank (can be

maximum =1) of each object; RANK

max

– the maxi-

mum value of the RANK for all sets of objects.

3.5 Formalization Description

The object of formalization is speciﬁc scientiﬁc stud-

ies. The result of formalization is a specialized

research-oriented subject area formed precisely from

existing research and allows to familiarize with the

specialized subject area. Any research essentially has

the same components (which are proposed to be sys-

tematized in the form of graphs) – introduction (land-

scape, object of research, subject of research, nov-

elty, etc.), methods (a set of methods that ensures the

achievement of a scientiﬁc result or measurement),

speciﬁc achievements and results (e.g., systems and

approaches developed or metrics) and discussion. All

components except the last one can be formalized us-

ing the IMRAD approach in such a way that they form

an ontology of the subject area of a speciﬁc ﬁeld of re-

search. Discussion, in its essence, is ﬁnding the place

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

582

Table 1: Description of the metadata on each ontology of proposed ontology model.

MD(L1) no metadata

MD(L2) {Class: Information about the ﬁeld; Type: String; Value: Description}

MD(L3) [MD(LR); MD(BMS); MD(M); MD(R)]= LR, BMS, M, R{Class: General information;

Type: String; Value: Describing and detailing of meaning results, methods, literature re-

view, etc.}

MD(L4)

∑

[MD(LR

);MD(BMS

);{ MD(M

);MD(R

)] = { Class: Essence of the name (speciﬁc

method, results, etc.; Type: String; Value: Describing of way of providing or speciﬁc mea-

sured parameter}

MD(L5)

∑

{Class: all metadata of speciﬁc study; Type: Number or String; Value: Text or number}

of this research in the system of scientiﬁc research –

that is, it is the process of comparing the results of re-

search, numerical and other data with existing other

data and providing explanations of the differences of

this speciﬁc stud. In fact, such processing is provided

by the ranking tools and the CIT Polyhedron alterna-

tive.

4 DISCUSSION

4.1 Case of Usage: An Example on

Biogas Production

So, for the speciﬁc case of biogas production studies

(Ivanov et al., 2019; Shapovalov et al., 2020a; Plyat-

suk and Chernish, 2014; Bochmann et al., 2020), it

seems relevant to use ontology for a speciﬁc ﬁeld (in

the form of {L2, L3, L4, L5}). In this case, a node

in the L2 line will be single and named “Studies on

anaerobic digestion”. It will be linked with nodes In-

troduction, Methods, Results, and Processing. As for

all other cases, Introduction will be divided into Basic

Metadata and Literature review (L3 level).

Basic Metadata will be linked with nodes Ob-

jects, Subjects, Aims, Practical Value, Scientiﬁc nov-

elty, Hypothesis, Keywords, Abstract, Conclusion

(L3 level).

Each of these nodes will be connected with spe-

ciﬁc nodes relevant to the set of the structured studies

(L4 level). Each speciﬁc L3 will have metadata with

general information on the described object. So, an

example of values of metadata in the “Basic meta-

data” elements node in the L4 level is shown in ta-

ble 2.

*verbs “are deﬁned” or “has provided” etc. and

articles “the”, “a” and “an” aren’t use due to their

huge vitiation and to provide better structuration and

to have more coincidences between nodes and meta-

data

Each such node will be connected with the study

where it was used (L5 level). For example, “Biogas

production from the poultry waste” or “Utilization of

the meat production wastewater using anaerobic di-

gestion”.

The Literature review node (L4 level) will be con-

nected with speciﬁc studies used in a set of studies.

Its name will be the name of the study (paper, ar-

ticle, conference processing, thesis, etc.), similar to

the name of the study used to provide structuration

with the addition of the publishing year. For exam-

ple, it can be named “Utilization of the meat produc-

tion wastewater using anaerobic digestion, 2011”. In

addition, each such node should be connected to one

of the few studies used to provide structuration (L5

level).

The most useful will be Methods and Results

nodes. They will be helpful to students and youth sci-

entists who want to be familiar with methods used in

the ﬁeld and set the measured parameters used in the

ﬁeld of science. Sure, the established scholars will

use such a tool too to increase outlook. The Materi-

als and Methods node will be divided into Methods,

Equipment, and Materials. An example of material

and methods and results nodes, their links and meta-

data are presented in table 3.

Each such subsidiary node is connected with a leaf

node that is a speciﬁc study. For example, the Pro-

cessing node has metadata with type link and its value

in the form of a link to Audit and Ranking tools for

the structured set of studies. Detailed algorithms of

its usage are described before.

Each work has metadata that mostly duplicates the

structure. For this, all numeric and semantic data of

the works is added to a node of the speciﬁc work it be-

longs to. Examples of the metadata of the leaf nodes

are presented in the table. It is foreseen to provide

automatically. For example, it will be necessary to

provide ﬁltering, Audit, and ranking. An example of

metadata and its classes (subclasses) of the speciﬁc

report node is shown in table 4.

Mathematical Interpretation of Educational Studentâ

Zs and Scientiﬁc Studies in Form of Digital Ontologies

583

Table 2: An example of “Basic metadata” elements nodes in L3 level and linked with them nodes in L4 level.

Parent’s node

(L3)

Metadata of the par-

ent’s node

Linked nodes (L4)

Objects

General

deﬁnition

of the

elements

of basic

metadata

“biogas production”, “inhibition”, “waste utilization”

Subjects “Effect of ammonium nitrogen content on biogas production”, “Opti-

mization of the process of waste treatment by optimization of the waste

destruction rate”

Aims “Provide mathematical modeling of the anaerobic digestion of high-

ammonium waste”, “Deﬁne of inﬂuence of the addition of spirulina to

the process of anaerobic treatment of straw”

Practical

Value

“Main kinetic parameters of the anaerobic digestion”, “Model of am-

monia effect on the anaerobic digestion”

Scientiﬁc

novelty

“Relation between ammonia content and biogas production”

Hypothesis

Keywords “Straw”, “Sludge”, “Meat wastewater”, “Biogas”, “Methane”, “Ammo-

nium nitrogen”

Abstract –

Conclusion –

Table 3: An example of material and methods and results nodes links and metadata

Parent’s node Metadata of the father’s

node (type: text)

Linked subsidiary nodes Metadata

Methods General information what is

methods

“Dry organic matter by frying”,

“Methane content in biogas using

gas chromatography”, “Free acid

content by titrimetric method”

Methodology of using of

each speciﬁc method (type:

text)

Equipment General information what is

equipment

“Digital microscope”, “Burette”,

“Gas chromatograph”

Description of each spe-

ciﬁc equipment (type: array)

Link to ontology of the

equipment (type: text)

Materials General information what is

material

“Straw”, “Sludge”, “Meat wastew-

ater”, “Water”

Description of each speciﬁc

materials (type: text)

Results General information what is

results

“Biogas”, “Methane”, “Ammonium

nitrogen”

Description of each mea-

sured parameter (type: text)

4.2 Role of the Proposed Model

Ontology models are the basis of the effective ontol-

ogy creative process. Such models like proposed and

others (for example, ontologies of educational envi-

ronments, will be useful to build a set of the differ-

ent ontologies and have similar conceptual states of

abstraction. Using such approaches and providing se-

mantic technologies can be useful to provide interop-

erability (Alnemr et al., 2010).

Sure, the proposed research focused on the on-

tology of the speciﬁc ﬁeld in the form of {L2, L3,

L4, L5}, but it is proposed to use an integrator of

the ontologies of ﬁelds and create general ontology

in the form of {L1, L2, L3, L4, L5}. The proposed

integration is important to provide transdisciplinary

(Dovgyi and Stryzhak, 2021). The proposed approach

will be useful and relevant for most ﬁelds. Anyway,

it will be very speciﬁc to process humanitarian data

where less standardization and numeric data, but it

seems that some automated tools like recursive re-

ducer (Stryzhak et al., 2018) can process and provide

structuration even in such ﬁelds.

4.3 Perspectives of Development

Currently, the proposed approach has a few user sto-

ries implemented by the proposed model. They are

helpful for all scientists, but as the development of

the proposed model was provided in the Navigational

center Of Junior Academy of Sciences of Ukraine,

it has much more advantages for youth students in-

volved in activities of the organization. The mathe-

matical interpretation of educational students and sci-

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

584

Table 4: An example of metadata and its classes (subclasses) of the speciﬁc report node.

Name of class Name of subclass Type Values example

Methods – Array “Dry organic matter by frying”, “Methane

content in biogas using gas chromatog-

raphy”, “Free acid content by titrimetric

method”

Results

Biogas content, ml/ g TS Number “305.15”

Methane content, % Number “55”

Ammonium nitrogen content, g/l Number “3.6”

Materials

Straw/TS content, % Number “95”

Straw/Ammonium nitrogen content, g/l Number “0.3”

Sludge/TS content, % Number “0.05”

Main metadata Keywords Array “Straw”, “Sludge”, “Meat wastewater”,

“Biogas”, “Methane”, “Ammonium nitro-

gen”

entiﬁc studies in the form of digital ontologies pro-

vides the possibility to easily manage information of

science studies to simplify ﬁnding of relevant studies

and simplify familiarization process with some spe-

ciﬁc subject area.

The proposed approach

1) allows very quickly (especially for a young sci-

entist) to research the subject ﬁeld related to this

ﬁeld of research by using → Introduction → Key-

words (contains the main terms of the subject ﬁeld

of speciﬁc research) and other components of the

Introduction (for example, scientiﬁc novelty for-

mulates the directions of research, which formu-

lates relevant research directions);

2) allows to process numerical research data using

the ranking tool and ﬁnd such works that are nec-

essary for research;

3) allows you to quickly familiarize with the existing

research methods used in this ﬁeld → Methods;

4) allows to quickly familiarize with the indicators

used in research in a speciﬁc ﬁeld (→ Results)

Communication with L5 vertices is essential be-

cause it is he who forms the novelty (since the ap-

proaches to the ontological display of subject area

have been known for a long time);

5) allows the researcher/student (young scientist) to

quickly ﬁnd practical examples where this or that

element of research is used – for example, quickly

ﬁnd all works where ammonia was measured us-

ing the Nessler method or works where graph the-

ory was used.

In addition, this approach has the potential for de-

velopment, which is as follows:

• the possibility of providing scientometrics based

on ontologies (similar to scientiﬁc databases) –

since it is possible to calculate how many times

a particular work has been referred to due to the

connections in such a taxonomy;

• the possibility of interoperability providing with

educational programs;

• the possibility of adding one’s own research for a

few clicks to the general ontology.

5 CONCLUSIONS

It is ﬁrstly proposed the model of ontology based on

IMRAD to provide a set of different studies that be-

long to the same ﬁeld and to provide generation of

the integrated ontology that collected the data of dif-

ferent ﬁelds. Using such a method will provide both

structuration of the set of studies by using speciﬁc ele-

ments of IMRAD that belongs to the set of the studies

of the same ﬁeld and processing such studies’ data.

A speciﬁc case of usage is shown in the example

creation of such ontology in the ﬁeld of biogas pro-

duction. It is shown in both model and example using

single sets of keywords, results, methods, etc., to pro-

vide structuring and data processing.

It seems relevant to provide additional further

studies of the proposed model to improve it and make

it even more automatized, for example, by using

weight mechanisms.

The proposed approach in case of providing prop-

erty infrastructure and widespread will provide inter-

operability of data located in papers. Therefore, it will

simplify providing of scitintiﬁc studies and simplify

determination of relevance and practic value of scien-

tiﬁc works. To provide such interoperability graphs

of speciﬁc ﬁelds should be created and proivded their

further merging. So, the onotologies type {L2, L3,

L4, L5} must be integrated into single one with form

of {L1, L2, L3, L4, L5}.

Mathematical Interpretation of Educational Studentâ

Zs and Scientiﬁc Studies in Form of Digital Ontologies

585

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