Scientific and educational studies can be structured using the formalisation of the IMRAD approach, providing data interoperability. This study focuses on using study results as part of a centralised informational weboriented educational environment. Structurisation was applied to two studies: “Development of a rational approach for utilising methane tank waste at LLC Vasylkivska chicken farm” and “Development of a strategy for utilising methane tank efluent”. Specific tools from CIT Polyhedron were used to process study data. The audit tool compares newly inputted data to existing taxonomies and highlights cases of full correspondence between elements of diferent works, such as study objects. The approach of integrating studies with educational ontologies, which is part of the centralised informational web-oriented educational environment, is described. The formalisation of this process is described using mathematical expressions.
Science now afects all aspects of human life more than ever before. The latest scientific developments are often quickly implemented in industry. However, scientific results are usually presented in humanreadable form rather than machine-readable format, making it dificult to process the knowledge using automated informational technologies.
The basic structure of a typical research paper is the sequence of Introduction, Methods, Results,
and Discussion (sometimes abbreviated as IMRAD) [
The most common form of scientific reporting is a written paper. Depending on the purpose, there
are several diferent paper types: Analytical Research Paper, Argumentative (Persuasive) Research
Paper, Definition Paper, Compare and Contrast Paper, Cause and Efect Paper, and Interpretative Paper.
The most common research paper types are shown in table 1 [
Nowadays, most papers (but not all) are systematised using scientometric databases. However, educational research reports, which use scientific methods, have not been systematised. Unlike pupils, scientists already know their field of research in detail and can determine their research hypothesis, and they can do further analysis by themselves. Students, instead, cannot do this. Automated informational tools can help students in these scientific discovery and analysis tasks. Someone poses a question and then collects relevant data from other researchers to analyse their diferent viewpoints.
The argumentative paper presents two sides of a controversial question in one paper.
The definition paper describes facts or objective arguments without using any personal emotion or opinion of the author.
Compare and contrast papers are used to analyse the diference between two viewpoints, authors, subjects or stories.
Cause and Efect Paper traces probable or expected results from a specific action and answers the main questions “Why?” and “What?”.
An interpretative paper requires one to use knowledge gained from a particular case study.
This type of research paper describes a particular experiment in detail.
This research paper demands the conduction of a survey that includes asking questions to respondents.
* Depends on the purpose of the article and the requirements of the journal, institute, teacher.
The STEM (Science, Technology, Engineering and Mathematics) approach may be interpreted as using the scientific method in an educational process while providing academic research. This approach has only recently been applied in countries such as Ukraine. There are various school competitions for scientific works, such as the competition on scientific articles of the Junior Academy of Sciences of Ukraine and international competitions (Intel ISEF). Also, the scientific method can be used during the creation of thesis papers (for masters’ degrees, bachelor’s degrees, etc.) and pupils’ research reports (for events noted before), or in simpler, but more common form of essays. In addition, students can report their results in scientific papers if the quality of their work meets scientific requirements. An overview of the types of educational research reports is presented in table 2. This paper focuses on the systematisation and processing of academic research reports. The problem to be addressed is the lack of a structuring mechanism that complicates the automated processing of such reports.
The active dissemination and use of diferent scientometric databases continue to increase the
convenience and eficiency of scientific data processing, structuring, and systematisation of research and
scientific results. Specialised databases for structural science information are an integral part of the
information-support system for any scientist. Scientometrics is the “quantitative study of science,
communication in science, and science policy” [
To increase the quality and performance of scientometrics, the ten principles of the “Leiden Manifesto
for Scientometrics” [
9. Recognise the systemic efects of assessment and indicators. 10. Scrutinise indicators regularly and update them.
Today, all existing scientometric databases can be divided into two major groups: international and
national [
As scientific publications continue to grow exponentially, the number of academic databases and
scientometric databases increases, which supports gaining insights into the structure and processes
of science [
By using scientometric systems, researchers’ contributions in informatics and scientometrics were
previously quantified [
• The degree of cooperation based on afiliation data.
The disadvantage of this research is that it is devoted only to scientific articles. The authors noted that their study could not cover students’ and pupils’ research reports because there is no single database where they are all located.
The application of the principles of the “Leiden Manifesto for Scientometrics” is stated and substantiated, providing transparent monitoring and support of research and encouraging constructive dialogue between the scientific community and the public. In this work, the bibliometric base, which corresponds to principles of the “Leiden Manifesto of Scientometrics”, has been created. The proposed bibliometric center did not address the systematisation of students’ and pupils’ research reports. Still, the authors noted the necessity of involvement of students’ and pupils’ research reports in their bibliometric center.
The approach of co-word analysis has been introduced, and its application in scientometrics is
substantiated in [
Another concept of analysis is iMetrics, or “information metrics”. Its application in scientometrics
is substantiated in [
Previously, ontological graphs were used to systematise scientific articles [
None of the scientometric database systems previously proposed [
This work aims to propose and justify using an ontological system, which permits the systematisation of scientific articles with all advantages of existing scientometric systems and without disadvantages of these systems, which at the same time will not be deprived of the functionality of current scientometric systems and will meet the Leiden Manifesto for Scientometrics.
As Proof of Concept (PoC), we propose to use the existing cognitive IT platform Polyhedron as
the technical basis for solving this problem. The core of the Polyhedron system consists of advanced
and improved functions of the TODOS IT platform described in previous works. The Polyhedron
is a multi-agent system that allows for transdisciplinarity and acts as an interactive component in
educational and scientific research [
Also, the proposed solution for the structuring of educational and research projects can be used
together with other modern developments in the academic field, like a virtual educational experiment
[
As was investigated before, the main elements of educational studies are represented by IMRAD
nodes and their specific subnodes related to a particular study [
{, , , } ∈ where – node of ontology that integrates data related to introduction; – subject of study: node of ontology that integrates data related to methods; – node of ontology that integrates data related to results; – results of study’s results processing.
Each scientific study contains specific data structured by IMRAD, and it may be represented as a set of tuples (corteges) that describe elements of specific studies. The equations 2 and 3 are used to describe representing two diferent studies structured by IMRAD:
Two diferent studies integrated into a single ontology will be described as the sum of IMRAD elements. Such representation is shown in equation 4:
⟨ , ⟩ = ⟨ , , , , , , , ⟩
In such case, some specific elements of studies are overlapping and other are not. For example, the Method section of two diferent studies represented in form of an ontology using IMRAD will be as follows:
= < , , , > = < , , , > = , , , = , , ∈ , ∈ , (1) (2) (3) (4) (5) (6) (7) (8)
In such representation and belong to both studies, and it is possible to use them as linking nodes to connect the two studies:
It is worth noting that such representation of studies leads to the ontologisation of the studies’ data. The most specific terms may be used to connect to diferent types of ontology-based knowledge, for example, educational programmes. However, such an approach was not conducted before. This study also aims to provide interoperability between diferent ontology-based knowledge systems using terms used in conducted studies and other knowledge systems (on the example of educational systems).
To create ontologies in Polyhedron, Google Sheets were used to collect and structure the information
by an expert who took the data manually (see example in figure 1). The sheets with research report
data (structure file and numeric/semantic data file) have been downloaded and saved in .xls format. The
ifles have been loaded to “editor.stemua.science”, part of Polyhedron. After that, the generation of the
graph nodes (in .xls) with their characteristics using the data structures in the file has been carried out.
The obtained graphs have been saved in .xml format and located in the database. The graphs have been
iflled with semantic and numeric information for ranking and filtering. Ontological edges (relations)
have been formed using predicate equations, as described previously in [
Considering that, e.g., proposed reports “A” and “B” are technical, the results of the reported works can
be used to analyse the rationality of the implementation proposed in the concrete project. For instance,
to ofer it, research reports “A” and “B” were also compared with each other using a ranking tool
applying the following criteria: “Short-term economic perspective”, “Long-term economic prospects”.
For creating a ranking, the ontologies have used the module “Alternative”, described in [
The ranking uses a grade scale from one to ten points to underline the relevance coeficient. The projects with a payback period of more than 25 years have been evaluated with 1 point, with 20-25 years of payback period with 2 points, from 15-20 years of payback period with 3 points, from 10-15 years of payback period with 4 points, 6-10 years of payback period with 5 points and with 1-5 years were evaluated as 6-10 points, respectively, by the “Economic attractiveness” criterion. A detailed evaluation for projects with 1-5 years is provided due to its utmost interest for the investor’s “payback time”, which determines investment expediency.
To provide an audit of the hypothesis of work “A” and “B”, the “standard” graph (with which the comparison is done) and the “comparison” graph (which is compared with the “standard”) have been created. The “standard” ontology graph contains the data on hypotheses, subjects, objects of research, keywords, and other parameters of the research reports done before. For the “standard” graph, each parameter was presented in a separate node. The content of this ontological graph “standard” is constantly updated and supplemented.
The nodes of the “comparison” graph have been represented as names of the works which need to be audited with the “standard” graph. The parameters of the work used to be audited with the “standard” graph have been located in the metadata of each separate node. The metadata type names were identical to the terms of the nodes of the “standard” graph to enable interaction between graphs.
The developed ontologies were saved in the same environment where elements of the centralised informational web-oriented educational environment were saved. Its features were used to provide interoperability with educational programs, methods, equipment, ontology-based didactical materials, and other ontology tools. As all such ontologies had the same graphs’ nodes names, we provided the integration between elements of the centralised informational web-oriented educational environment and proposed structurisation of academic studies. We used the same nodes and provided links with each graph that it contains. For example, the term temperature regime that is used in educational programmes is connected with all academic programmes in physics (part of topic energy, thermal energy), chemistry (amount of topic energy of reaction), and scientific study graph that was conducted by a young researcher on biogas production research called temperature regime. Also, we can link this term with a method called ensuring of requested temperature by a thermostat and with equipment dry air thermostat. So, for this, we are using the term temperature regime to provide an interdisciplinarity approach that is related to diferent fields of science and to varying types of data (educational plans, equipment that is used, specific methods and specific personal studies).
The general concept of the proposed ontology-based graph model for Polyhedron research reports has a specific, logically connected structure and can be represented as an ontology. After structuring, it is possible to describe the reports’ content in a simpler to understand presentation form. Besides, most results can be domain-specific for each industry, and if the current standards are correctly identified, these values will be easy to compare. Also, most research in one field often uses the same equipment, materials, chemicals, standard methods of analysis, literature, etc., which allow comparing these works with each other and correctly structuring them.
However, the main advantage of the proposed approach (besides structuring the research) is
processing results in terms of separated result parameters of the reports. This supports data analysis,
further processing using ranking, and semantic data interoperability. The separation of numeric data
and its location metadata class is possible due to the addresses of the same field, describing the process
using the same (or similar) parameters of the process description and result parameters description.
For example, for most reports on anaerobic digestion, the process parameters are temperature, type of
substrate, reactor volume, moisture content, initial pH; the characteristics of eficiency of the process
are biogas yield, methane content, average pH during the process, destruction process, etc. [
As all research reports will be simplified, this approach will be especially relevant for pupils and novice researchers with further potential use in the educational process or to streamline the literature review process for new academic research.
For example, the object of the study of research report “A” is the disposal of anaerobic efluent. The subject of the report’s research is the Cultivation of Chlorella Vulgaris microalgae on efluent obtained after methane fermentation. The study aims to develop a method of growing Chlorella Vulgaris in the efluent after methane fermentation. The practical significance of this scientific work is the results, which will contribute to the spread of biogas technologies. Also, the proposed approach makes it possible to increase the economic benefits of utilising chicken manure by converting the anaerobic digestion efluent into microalgae with a wide range of applications. The scientific novelty of that research report is a method of utilisation of anaerobic digestion efluent by using microalgae, also had obtained cultures of Chlorella Vulgaris that had adapted to the anaerobic digestion efluent. The working hypothesis was that the efluent obtained after anaerobic digestion could be used as a nutrient medium for microalgae Chlorella Vulgaris.
The object of the research report “B” study is the disposal of anaerobic digestion efluent. The subject of the research is the processing of anaerobic digestion efluent into humates by the autocatalytic catalysis method. The study aims to establish regularities of processing the solid fraction obtained during the methane fermentation of chicken manure by the autocatalytic catalysis method. The practical significance of this scientific work is that the study indicates the possibility of acquiring salts of humic and fulvic acids by the autocatalytic catalysis method. This approach makes it possible to increase the economic benefits of chicken manure disposal by converting the anaerobic digestion efluent into a more valuable product with a wide range of applications. Its scientific novelty is that potassium humate had firstly obtained from anaerobic digestion efluent. For the first time, the eficiency of receiving humates from the solid fraction of anaerobic digestion was investigated, and the main regularities of the process were determined. The working hypothesis was that the solid fraction of methane fermentation of chicken manure can be recycled by the autocatalytic catalysis method.
(a) (b)
For both research reports, “A” and “B”, chicken manure from the same poultry farm has been used as a substrate for anaerobic digestion. In this case, chicken manure and its efluent, which has been obtained by anaerobic digestion, were analysed by the same methods and indicators. Such indicators were: • “Ash and dry content”. • “Determination of volatile fatty acids content” (in terms of acetic acid).
• “Determination of ammonium nitrogen content with Nessler’s reagent”.
The equipment used to determine these indicators was also the same. Therefore, how these works can be structured and integrated using the cognitive IT platform Polyhedron has been considered. All examples of ontological nodes in the obtained graphs for further potential information processing are presented in Table 4.
To present possibilities and systematisation of the research report, we have applied an ontological
taxonomy for students’ works “A” and “B”. The general view of the obtained graphs is shown in figure 3
[
The “Materials and methods” node, which contains all the materials, was used to perform the
experiments. Every approach has been divided into the separate attribute of the node. This allows
concentrating the reader’s attention, and it helps to process the data with each other. For further
researchers, this mechanism will be described in detail. The general view of both works’ “Material and
Methods” node is shown in figure 3 [
(a) (b)
For each ontological node that duplicates sections of the research report, and that contain specific indicators after analysing, additional separate leaf nodes with these results have been created.
In this leaf node, all the issues are held in the form of semantic and numeric data. These results are automatically available for filtering, auditing and ranking. An example of this leaf node is shown in ifgure 4.
The audit tool [
The “comparison” ontology has been created with the child nodes, which contain the following hypothesis: the efluent obtained after anaerobic digestion can be used as a nutrient medium for microalgae Spirulina Platensis (hypothesis 1), and the efluent obtained after anaerobic digestion can be used as a nutrient medium for microalgae Chlorella Vulgaris (hypothesis 2), the efluent obtained after anaerobic digestion cannot use it as a nutrient medium for microalgae Chlorella Vulgaris (hypothesis 3). The hypothesis 2 node also contains some metadata. This ontology also had a simple structure without branches with the parent node, the “Hypothesis test system”. The general view of the obtained ontology of the comparison and the ontology of the standard in taxonomic form is shown in figure 5.
The system has checked whether the hypothesis is true or false by using the audit function. Those indicators which do not correspond to the standard have been coloured red. Thus, this solution will allow testing the idea of these scientific works and checking other metadata that have already been set by using information from the “Abstract” node (figure 5b).
Research report “A” and research report “B” have been compared with each other by the following criteria “Short-term economic perspective”, “Long-term economic prospects”. According to section 2 of the research report “A”, the payback period of project “A” is five years, which corresponds to 6 points according to the criterion “Economic attractiveness”. This parameter is better for the project described in report “B” with a payback period of four years and three months, which corresponds to 5 points on “Economic attractiveness”. The system provides raking of the results. If there is a large amount of data, the instrument will be helpful to quickly and efectively evaluate the projects on “Economic attractiveness”. Besides, in further research, the other criteria will be justified and used to provide data Viktor B. Shapovalov et al. CEUR Workshop Proceedings management on the educational research, which will make the tool more functional.
As it was shown before, the preleaf nodes (L4) are terms (main ontology elements) that are very promising to use in terms of interoperability with other ontologies of subject areas, for example, with educational programmes that in that term will be the additional instrument for Centralised informational web-oriented educational environment.
So, such connection with the centralised informational web-oriented educational environment concept and ensuring interdisciplinarity is described by formulas. Each ontology is based on the conceptualisation of terms. It means that each ontology is described as a tuple (cortege) of terms from the field it contains:
So, we can describe ontology of educational programme, ontology of equipment that being used, ontology of method and ontology of educational studies as further:
=< > 1 = 1, 2, 3, 4, 5 2 = 1, 3, 5, 6 3 = 3, 5, 6, 7 4 = 1, 3, 8, 9, 10, 11, 12
1 ∈ 1, 2, 4 3 ∈ 1, 2, 3, 4
5 ∈ 1, 2, 3
As seen for equations, some ontologies have cross-terms that will provide inoperability with CIWOEE and interdisciplinarity. The terms that are cross-terms will be used by the user to transfer from elements (nodes) of one ontology to aspects of another. For this example, leaf or sub-leaf (in the case of educational studies’ ontology; such as specific methods, keywords, objects, etc.), 1, 3, and 5 are cross-terms:
As shown in equations 10-13, the terms of scientific study ontology, such as specific methods, keywords, objects, etc., are used to provide a link with terms of educational programmes. Terms of studies that may be used to link the ontology of scientific studies with the ontology of educational programmes are shown in figure 8.
A similar situation is also related to educational programmes. There is an ontology that systemises the data on the knowledge field related to chemistry using schools’ educational programmes. It also consists of terms that are presented in the form of nodes. The general view of academic programmes’ ontology related to chemistry educational programmes in Ukraine and phrases that may be used to link with the ontology of scientific studies is shown in figure 9.
Therefore, some terms may be related to both educational programmes and scientific studies ontologies. In this case, such links allow using subnodes (keywords, methods, etc.) to find scientific studies related to this term. The exact words to provide interoperability between educational programmes ontology and scientific studies ontologies are shown in figure 10. As can be seen, the terms “methane” and “biogas” are related to both educational programmes and scientific studies and, therefore, they are used to link these ontologies.
(9) (10) (11) (12) (13) (14) (15) (16)
The proposed database follows the “Leiden Manifesto for Scientometrics.” In the obtained ontological
database, quantitative evaluation can be supported by qualitative expert assessment. Additionally,
this ontological database can unite the research missions of the institution, group, or researcher and
protect excellence in internally relevant research. The ontological form of research reports can keep
data collection and analytical processes open, transparent, and straightforward. Because all metadata is
contained in a separate node that can be expanded and supplemented. Thus, the obtained ontological
database can also account for variations, e.g., in publication and citation practices. It can provide a base
assessment of individual researchers’ qualitative judgement of their portfolios. Because all ontological
graphs are validated by experts, in this way, it is possible to avoid misplaced concreteness, including false
precision, and recognise the systemic efects of all assessments and indicators. In addition, indicators can
be scrutinised regularly and updated in the obtained ontological database. Furthermore, the proposed
ontology-based research reports can be integrated into a single environment – ontology repositories, as
suggested before [
The process starts with paper creation. For this stage, we can use various text editors, for example, Word or Google Docs. Then an expert or author of the paper will formulate metadata, which is necessary for the ontology. For this purpose, the author will use Microsoft Excel or Google Sheets. Then, an editor needs to add information to the graph. In our case, the IT Platform Polyhedron is used for this. And last but not least, it is possible to use the “Alternative” system, which includes Audit, Filtering, and Ranking instruments. All proposed tools are illustrated in the workflow diagram figure 11.
It is worth mentioning that this methodology of the centralised information web-oriented educational environment of Ukraine has been developed, and with the ontological approach is more systematic now. Educational programmes are essential to the world picture that is given to people during education, so they contain all basic terms that may be used to systematise other efilds of human activities, including scientific studies. Like researchers, pupils interested in terms can also use such specific term nodes to continue their studying by investigating the studies conducted.
An ontological approach to scientific work systematisation has been proposed, assuring compatibility. A system for arranging research reports based on digital taxonomies (ontologies) has been created. It allows users to construct node hierarchies utilising the natural structure of the reports. Concrete parameters were added to the nodes as metadata (semantic, numeric, images, and links) to enable Polyhedron tools processing. Ranging and filtering were employed to handle semantic and numerical metadata. The obtained results allow for interchange across various study reports (including educational). The “Leiden Manifesto for Scientometrics” is the acknowledged ontological method.
Further study will improve interoperability across research works by developing a single taxonomy that provides hierarchisation using the same methodologies, literature, and report findings and its processing using both methods suggested in the research and newly developed ones.
For the first time, it presents the concept of integration of scientific studies ontologies with educational programmes that make them more usable for both students and young researchers. The proposed approach aligns with the centralised informational web-oriented educational environment concept. Declaration on Generative AI: The authors have not employed any Generative AI tools.