=Paper=
{{Paper
|id=Vol-3013/20210353
|storemode=property
|title=A Taxonomic Representation of Scientific Studies
|pdfUrl=https://ceur-ws.org/Vol-3013/20210353.pdf
|volume=Vol-3013
|authors=Yevhenii Shapovalov,Viktor Shapovalov
|dblpUrl=https://dblp.org/rec/conf/icteri/ShapovalovS21
}}
==A Taxonomic Representation of Scientific Studies==
<pdf width="1500px">https://ceur-ws.org/Vol-3013/20210353.pdf</pdf>
<pre>
A Taxonomic Representation of Scientific Studies
Yevhenii B. Shapovalov and Viktor B. Shapovalov
National Center Junior Academy of Sciences of Ukraine, Dehtiarivska St, 38/44, Kyiv, 04119, Ukraine

                Abstract
                Taking to account the problem of the high amount of scientific data, it is rel-evant to develop
                methods of its structuration and processing. Using ontology graphs is the perspective modern
                way to provide. Taking to account that most studies is 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 IMRAD have been de-scribed. To get
                the necessary level of abstraction IMRAD elements as part of a set of specific study were
                decomposed as levels of abstraction from L1 (general integration node with generalized data)
                to L5 (specific papers with specific data) depended on the abstraction. The content of each
                node in form of metadata and its further processing is described. The specific way of the usage
                of proposed modes has described on the example of the describing studies in the field of biogas
                production. The proposed approach can be used in a single field or be even more integrated
                and be devoted to the structuration of the works in different fields.

                Keywords 1
                ontology, IMRAD, structuration, scientific studies, biogas

1. Introduction
    The data nowadays generated with huge intensity. Due to this, now, Big Data processing is a trend
[1, 2]. Processing of the huge amount of data in real life is complicated by high gain of publishing of
scientific studies.
    Considering the development of the STEM-education, studies are provided not only by experienced
scientist but by youth. Such huge amount of the studies generated complicated task to process such
data. One of the problem of low spreading and usage (in example of Ukraine [3, 4]) may be related to
difficulties.
    Now, scientific studies are published in different forms of reports, such as articles, conference
proceedings, books, etc. However, its process is hard due reports are low-structured. Sure, they all build
by similar structure named IMRAD [5, 6]. It includes requirements for the paper to consist of some
generalized Introduction, describing of used Materials and Methods, naming the Results of the study
and is Discussion by comparing with other scientific materials or providing of use cases. However, it
seems not enough. Here just some examples of problems due to it:
        •    hard to start the researcher career due to complicated process of understanding of the methods and
             equipment that need to be used in specific fields of study
        •    hard for youth scientists to understand main parameters that has measured to provide study analysis
        •    harder for experienced scientists to analyze and collect data of new studies.
    These are only very few cases that are a problem due to high amount of data of scientific studies.
However, these cases are makes relevant to develop new methods to provide better structuration and
data processing of scientific studies.
    There are few solutions for this problem that provides automate science data processing [7–10], but
it seems that they do not take to account IMRAD. One of the methods that is relevant to use to solve
the problems is ontology taxonomies [11–14] with semantic technologies [15]. Also, ontology
taxonomies has a lot advantages such as possibility to combine with other types of materials [16]

ICTERI-2021, Vol I: Main Conference, PhD Symposium, Posters and Demonstrations, September 28 – October 2, 2021, Kherson, Ukraine
EMAIL: sjb@man.gov.ua (A. 1); svb@man.gov.ua (A. 2)
ORCID: 0000-0003-3732-9486 (A. 1); 0000-0001-6315-649X (A. 2)
             ©️ 2020 Copyright for this paper by its authors.
             Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
             CEUR Workshop Proceedings (CEUR-WS.org)
including interactive and web-based courses [17], other information technologies [18, 19] and GIS [20].
This research aims to develop the model that can structure the set of the studies using IMRAD.
    Previously, it was proposed to provide support using ontologies for single specific study, but not to
create glossaries and structured sets of data. To provide it tools Open provenance, Ontologyt and EXPO
[21] were developed. Another ontology solution in the field of science is MoKi that provides creation
of wiki-based information scientific sources [22, 23]. Sure there some specific ontology tools such as
Gene ontology [24] or Centralized educational environment [25]. 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 model of ontology has developed using of main principles of Graph Theory, Set
Theory, and a Theory of Abstraction [26]. The graph was modeled using a simple hierarchical algorithm
that foresees using only nodes and links. So, such a model further may be updated using the more
extended graph building tools such as weight coefficients. However, without simple modeling,
providing it will not be possible. To provide structuration generally accepted structuring method
IMRAD has proposed and used.
   To model data processing was developed taking to account processing possibilities of the
Polyhedron [20, 27, 28] system due it has some advantages compare well known Protégé [29, 30] and
OWL tools [31–33]. The principles of cognitive IT-platform’s tools Filtering, Audit, and Ranking to
provide decision-making data processing tools were described in form of mathematical model (set of
equitation) [2, 25, 34].



3. Results
3.1. Methods of the research
   As was noted before, IMRAD is used to prepare science papers. So, to provide structuration it is
possible to use parent nodes that represent IMRAD components. IMRAD - Introduction, Methods,
Results, and Discussion. The discussion part can't be structured by ontology because it contains the
analysis and comparing of the obtained data. That is why discussion will be represented as the
processing of the results.
                                         ¬ (D∈ REP) ⇒(P∈ REP)                       (1)

      where REP – report (or set of report) D – discussion of report’s results; P – processing of the
                                      results of a set of studies.

    Approximate, ontology can be devoted to a specific field of science or integrate different fields. In
depended 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 reports” and its subsidiary 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
the element of IMRAD. And the leaf node will be a set of the specific researches belongs to the field.
Let’s name each level with L symbols taking to account position in hierarchy:
    L1 – general name of parent’s node “Scientific reports”; L2 – Name of field of the reports; L3 – Part
of IMRAD; L4 – Specific representation of IMRAD (specific method, used materials, specific type of
the results); L5 – Specific reports where were used specific representations of IMRAD L4.
    Therefore, the hierarchy on the specific study will have a form of {L2, L3, L4, L5} and the general
ones will have a form of {L1, L2, L3, L4, L5}. Interoperability 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 fore-sees that graphs will be constructed as a general graph in 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 form of {L2, L3, L4, L5} and merge them. Schematic representation of the general
ontology is shown in and taxonomy of the specific field is shown in Fig. 1.




                       (a)                                                   (b)
 Figure 1: The taxonomy of the general science study ontology (a) and taxonomy of the specific field
science study ontology (b); where LR1, LR2, M1, R1, R2 – are abstract classes of literature review (LR),
Methods and results of object; “

     As an alternative and a more human-readable way to provide abstraction is to revert this model and
begin by L5 and end by L1. In this case, ontology will have such structure {L5, L4, L3, L2, L1}. Same
rules are relevant for second case.
     However, the main disadvantage of such a graph is obvious and is the consequences of the structure:
the leaf node SR (“Scientific reports”) will be not the most useful for users.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
qualifying work evaluation (Ph.D. or Master’s reports). 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 was obtained by the researcher. In this research, we’ll use the first way to provide hierarchies in
form of {L2, L3, L4, L5}, and {L1, L2, L3, L4, L5}.
     As it can be seen, the general science report ontology is significantly more complicated due to links
between L1 and L2 level, and also there will be some problems with a huge number 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 2 levels of links “one to many” (see Fig
1 (a), links between L2 and L3 level and links between L4 and L5 levels) compare to only one in case
of specific ontology (see Fig. 1(b), only links between L4 and L5 levels). It may be unreasonable to
create a complicated graph Therefore, it seems relevant to provide both types of hierarchies. To provide
it, the ontologies should be created 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
specific study 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 (BMD) and literature review (LR), it is possible to represent the
introduction as further:
    Basic metadata of the study node linked with graph nodes that characterized the basic data on the
study such as hypothesis, object, subject, practical value, and scientific novelty. And so, a node of the
report basic metadata of study can be presented as a further equation:
                                                  𝑛
                                       BMD= ∑𝑖 {𝐻𝑖 , 𝑂𝑖 , 𝑆𝑖 , 𝑃𝑉𝑖 , 𝑆𝑁𝑖 }              (3)
   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 specific study; SC – the scientific novelty of each
specific study.
   Lest presents each work of the set of the Introductions, Methods, Results, and Pro-cessing of the
data (Discussion). Then each work will be represented as a set of these elements that will be relevant
only for each specific study:
                                                        𝑛
                                               SI = ∑𝑖 {𝐼𝐼𝑖 , 𝑀𝐼𝑖, 𝑅𝐼𝑖, 𝑃𝐼𝑖 }                        (4)
                                                       𝑛
                                             SII = ∑𝑖 {𝐼𝐼𝐼𝑖 , 𝑀𝐼𝐼𝑖, 𝑅𝐼𝐼𝑖, 𝑃𝐼𝐼𝑖 }                     (5)

    So, these articles can be integrated in a single ontology using IMRAD. Such ontology will be a
single set of elements of both studies that related to single area:
                                  𝑛                    𝑛
                           O=∑𝑖 {𝑆𝐼 , 𝑆𝐼𝐼, } = ∑𝑖 {𝐼𝐼𝑖 , 𝑀𝐼𝑖, 𝑅𝐼𝑖, 𝑃𝐼𝑖 , 𝐼𝐼𝐼𝑖, 𝑀𝐼𝐼𝑖 , 𝑅𝐼𝐼𝑖, 𝑃𝐼𝐼𝑖 }   (6)

    The main advantages of using such structures are that some parts of introduction (for example
keyword), materials and methods and results elements (entities and measured parameters) of
studies/reports 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 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 specific area to
which set of the studies belongs (L2=∑𝑛𝑖 𝑅𝑆𝐼𝑖 , where L2 – specific area and RS – set of the studies
representing). The L2 node is linked with I, M, R, P nodes (representing IMRAD). Each IMRAD node
is linked with a specific node (such as ammonia determination by Nessler’s method (for methods) or
“chicken manure” or “glycerine” (for subjects)) that belongs. And each specific IMRAD type is linked
with leaf nodes of ontology – specific studies where such entities were used.
    In this case, few reports (REP1, REP2, and REP3 that belong to L5) will be integrated by some of
the methods or results (M1, R1, R2 that belongs to L4). So, the L4 level will be used to provide the
structuration of the reports (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 to define in which reports belong to
L5 specific method, result, etc. that belong to L4 were used.
    Сoinciding of the studies elements (for example methods) may be represented as single intersections
of such elements in few studies:
                                                MI={Ma, Mb, Mc, Md}                                   (7)

                                                  MII={Mb, Md, Mf}                                    (8)

    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 SI and SII will contain
information on the specific case of its usage and measured parameters using it. For example, same will
be a hierarchical way of representing and using the keywords.
    This will be useful especially for students and young scientists that looking to find methods (MI)
and parameters that can be used in specific fields and their usage in practice. Also, this way provides a
list of the parameters and methods that used in specific fields.


3.2.    Metadata
    Metadata of each work will be used for processing the data. It may be included for each node.
Metadata of L4 nodes will represent the general information (for example, the essence of the method
itself), and the resulting leaf nodes 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 it's class:
“Ammonium nitrogen content, g/l). And so, metadata with the same class will be processed by using
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 Ei contains metadata with the level of
abstract that corresponds to a number of levels; for level 1st – it will be the most abstract metadata and
for 5th – it will be the most specific.
   As it can be seen all data in levels L1-L4 contains generalized metadata and wouldn’t be used for
processing of specific study, but just used to get generalized abstract information on entities used in
specific fields. Only L5 level contains metadata that related to specific study and will be used for further
processing.
   Specific mechanisms “Filtering”, “AUDIT” and “RANKING” of cognitive IT solution Polyhedron
are used to provide processing of the information. It will be used for the case when different reports
will have the same Class and Type of information, but different value:
                               {Class: C1; Type: Number; Value: V1} ∈ REP1                   (9)

                               {Class: C1; Type: Number; Value: V2} ∈ REP2                 (10)

   And the values V1, V2, and V3 can be equal or not equal. “Filtering”, “AUDIT” and “RANKING”
can be used to process the data. Filtering can be described by function if:
   If (Vmin<V< Vmax) then (display nodes with such V) or If (V=Vset) 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 be also described as a function if:
   If (Vi=Vset) than (mark red such Vi); do it for all Vi.
   The ranking is much harder and can be described as:
                                                                         𝑉𝑖
                                       RANKabs (i)=∑(ORi×IMPi×                 ),            (11)
                                                                        𝑉𝑚𝑎𝑥

where RANK abs (i)- ranking rank in absolute value for i’s node ORi – orientation maximum or
minimum for metadata of I’s object (can be +1 or -1); IMPi – importance coefficient for metadata of
I’s object; 𝑉𝑖 – the value of metadata of i’s object; 𝑉𝑚𝑎𝑥 – maximum value of the set of metadata.
                                                      RANKabs (i)
                                             RANKi=                 ;                      (12)
                                                      RANK𝑚𝑎𝑥

  where RANKi – the relative value of the rank (can be maximum =1) of each object; RANKmax – the
maximum value of the RANK for all sets of objects.




4. Discussion
    So, for the specific case of biogas production studies [35–38], it seems relevant to use ontology for
a specific field (in 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 Introduction, Methods, Results,
and Processing. As for all other cases, Introduction will be divided to Basic Metadata and Literature
review (L3 level).
    Basic Metadata will be linked with nodes Objects, Subjects, Aims, Practical Value, Scientific
novelty, Hypothesis, Keywords, Abstract, Conclusion (L3 level).
    Each of these nodes will be connected with specific nodes relevant for the set of the studies (L4
level) that was structured. Each specific L3 will have metadata with general information on the
described object.
    Each of such nodes will be connected with study where it was used (L5 level). For example, “Biogas
production from the poultry waste” or “Utilization of the meat production wastewater using anaerobic
digestion”.
    The Literature review node (L4 level) will be connected with specific studies used in a set of the
studies. Its name will be the name of the study (paper, article, conference processing, thesis, etc.) similar
to the name of the study used to provide structuration with the addition of the publishing year. For
example, it can be named “Utilization of the meat production wastewater using anaerobic digestion,
2011”. Each such node should be connected to the one of few studies used to provide structuration (L5
level).
    The most useful will be Methods and Results nodes. They will be useful to students and youth
scientists who want to be familiar with methods used in the field and set the measured parameters that
used in the field of science. Sure, the established scholars will use such a tool too to increased outlook.
The Materials and Methods node will be divided into Methods, Equipment, and Materials.
    Each such subsidiary node connected with a leaf node that is specific study. The Processing node
has metadata with type link and its value in form of a link to Audit and Ranking tools for the set of
reports that are structured.
    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 belongs to. Examples of the metadata of the leaf
nodes are presented in the table. It is foreseen to provide automatically. It will be necessary to provide
filtering, Audit, and ranking.
    Ontology models are the basis of the effective ontology creative process. Such model like proposed
and others (for example ontologies of educational environments, will be useful to build set of the
different ontologies and have similar conceptual states of abstraction. Using such approaches and
providing of semantic technologies can be useful to provide interoperability [15].
    Sure, the proposed research focused on the ontology of the specific field in form of {L2, L3, L4,
L5}, but it is proposed to use integrator of the ontologies of fields and create general ontology in form
of {L1, L2, L3, L4, L5}. Such integration is important to provide transdisciplinary [39]. In general,
proposed approach will be useful and relevant for most of the fields. It will be very embarrassingly to
process humanitarian data where less of standardization and numeric data, but it seems that some
automated tools like recursive reductor [13] can process and provide structuration even in such fields.


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