Ontological system processing of databases of scientific publications
Development of theories, methods and algorithms for the discovery and formation of new knowledge always was one of the most important tasks for any researcher, especially if they actively working on creation of new scientific publications. There is no universal language to describe formally concepts...
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PROBLEMS IN PROGRAMMING
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| author | Palagin, O.V. Petrenko, N.G. Boyko, M.O. |
| author_facet | Palagin, O.V. Petrenko, N.G. Boyko, M.O. |
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| description | Development of theories, methods and algorithms for the discovery and formation of new knowledge always was one of the most important tasks for any researcher, especially if they actively working on creation of new scientific publications. There is no universal language to describe formally concepts (knowledge) and systemology of transdisciplinary scientific research. Because of this, researchers have a set of urgent problems, and one of them is the way of speeding up the process of finding information (in the form of cognitive-structure) in their own sources. Ontological system for processing of databases of scientific publications created to solve this problem for a researcher, who have from tens to hundreds of scientific papers published. We are unaware of search systems, which would provide the same information for a researcher in such a short time. Ontological system implements technologies of Information Retrieval and Knowledge Discovery in Databases with accent on technologies and instruments such as Semantic Web and cognitive graphics. Development of such ontological system have three stages. On the first stage instruments for system development created, methods and algorithms of interaction between system components "User ¾ Knowledge engineer ¾ Remote endpoint", also data added to the system at this stage. On the second stage task of multimedia presentation for conceptual and figurative structures, described in scientific documents, solved. Gaining new knowl- edge problem solved on the third stage.Prombles in programming 2022; 3-4: 161-170 |
| first_indexed | 2025-07-17T09:41:07Z |
| format | Article |
| fulltext |
161
Моделі і засоби систем баз даних та знань
UDC 004.318 https://doi.org/10.15407/pp2022.03-04.161
ONTOLOGICAL SYSTEM
PROCESSING OF DATABASES OF SCIENTIFIC
PUBLICATIONS
Oleksandr Palagin, Mykola Petrenko, Mykola Boyko
Розроблення теорій, методів й алгоритмів виявлення та формування нових знань завжди займало одне з центральних місць у
будь-якого наукового співробітника, тим паче якщо він активно працює над створенням нових наукових публікацій. Відомо, що
універсальної мови формального опису концептів (знань) та системології трансдисциплінарних наукових досліджень не існує.
А тому перед науковцями стоїть ряд першочергових проблем, в тому числі проблема значного пришвидшення отримання на-
уковим співробітником необхідної йому когнітивно-структурованої інформації із своїх джерел. Онтологічна система оброблення
баз даних наукових публікацій саме так орієнтована на наукового співробітника, у якого в наявності опубліковано від декількох
десятків до сотень наукових праць. Нам невідомі пошукові системи, які змогли б у максимально стислий термін надати науковому
співробітнику таку інформацію. Онтологічна система реалізує технології Information Retrieval і Knowledge Discovery in Databases
з акцентом на технології й інструментарій Semantic Web та когнітивної графіки. Розроблення такої онтологічної системи при-
пускає три стадії: на першій стадії створюються інструментальні засоби реалізації системи, методики й алгоритми взаємодії
системи «Користувач Інженер зі знань Віддалена прикінцева точка» та наповнення її даними; на другій стадії вирішуються
задачі мультимедійного подання образно-понятійних структур, що описані в наукових документах; і на третій стадії – вирішення
проблеми добування нових знань.
Ключові слова: Трансдисциплінарні наукові дослідження, Технології Semantic Web, Онтологічний інжиніринг, База даних на-
укових публікацій.
Development of theories, methods and algorithms for the discovery and formation of new knowledge always was one of the most impor-
tant tasks for any researcher, especially if they actively working on creation of new scientific publications. There is no universal language
to describe formally concepts (knowledge) and systemology of transdisciplinary scientific research. Because of this, researchers have a set
of urgent problems, and one of them is the way of speeding up the process of finding information (in the form of cognitive-structure) in
their own sources. Ontological system for processing of databases of scientific publications created to solve this problem for a researcher,
who have from tens to hundreds of scientific papers published. We are unaware of search systems, which would provide the same informa-
tion for a researcher in such a short time. Ontological system implements technologies of Information Retrieval and Knowledge Discovery
in Databases with accent on technologies and instruments such as Semantic Web and cognitive graphics. Development of such ontological
system have three stages. On the first stage instruments for system development created, methods and algorithms of interaction between
system components “User Knowledge engineer Remote endpoint”, also data added to the system at this stage. On the second stage
task of multimedia presentation for conceptual and figurative structures, described in scientific documents, solved. Gaining new knowl-
edge problem solved on the third stage.
Keywords: Transdisciplinary scientific research, Semantic Web technology, ontological engineering, scientific publications database.
Introduction
There is multiple applications, for searching information in different databases (DB), including spe-
cialized applications. Most of these applications do not take into account a cognitive aspect of data processing,
needed for creative approach, in particular for a researcher (RSR).
As a separate problem stands multimedia (conceptual and figurative) presentation of the search results, and
their comparison with conceptual structure of subject area (SA, Knowledge Domain), this interests us for purposes of
gaining new knowledge. For scientific research, it is relevant to process scientific publications of one author, authors
of scientific unit or institute by using Semantic Web technology.
Ontological system (OS) for processing of databases of scientific publications (DBSP) uses technologies
of Information Retrieval and Knowledge Discovery in Databases with accent on technologies and instruments of
Semantic Web and cognitive graphics [1–3]. This technology and corresponding instruments allow creating mul-
timedia presentation of conceptual and figurative structures, which described in scientific papers. Semantic Web
technologies allow creation and processing for RDF repository of scientific publications, development of local
and/or remote endpoints, assembling and execution of SPARQL-queries. Of the entire Semantic Web technolo-
gies multitude we need to highlight SPARQL-technology, which allows for a researcher (RSR) to create queries
of arbitrary complexity, and to receive response, including all kinds of information.
Generalized diagram for development of OS DBSP shown at Figure 1. It includes preparation stage block and
blocks of main stage with variations A, B and C. Preparation stage described in details at [1]. At the same place ontol-
ogy graphs of SA is given and one of SP, which serve as data for implementation of main stage, variation B, phase 2.
We know about personified knowledge database of researcher, in which a sum of functional capabilities de-
clared, this capabilities support processes of scientific and creative activity [2]. Such personified knowledge database
is:
̶ a tool that supports scientific research, and one of the central directions of practical informatics development
[4, 5];
© О.В. Палагін, М.Г. Петренко, М.О. Бойко, 2022
ISSN 1727-4907. Проблеми програмування. 2022. № 3-4. Спеціальний випуск
162
Моделі і засоби систем баз даних та знань
̶ a development of knowledge system for RSR, for purposes of new knowledge gain (or arrangement of exist-
ing knowledge, error checking and checking for contradictions etc.) [6–9];
̶ one of the main subsystems for the modern system of research design [10], automated workplace for RSR [4];
̶ one of the main elements for creation of permanent canonical knowledge [11] and support for knowledge
oriented information system functioning [12].Моделі і засоби систем баз даних та знань
Preparation stage
RDF storage of SP creation
with help of Protégé tools.
Files {SPn.owl}, n=(1,N)
Main stage.
Variation А.1
Local endpoint based on
SPARQL processor ARQ
Main stage.
Variation А.2
Local endpoint based on
The Protégé SPARQL processor
Main stage.
Variation В
Remote endpoint based on Apache Jena Fuseki
server.
Phase 1. SPARQL user queries execution.
Phase 2. Multimedia visualization of results
for user queries.
Phase 3. Manipulation by elementary senses
with purpose of gaining new knowledge
Main stage.
Variation С
Arbitral remote endpoint on the Internet
Figure 1. Generalized diagram of OS DBSP development
We know about personified knowledge database of researcher, in which a sum of functional capabilities
declared, this capabilities support processes of scientific and creative activity [2]. Such personified knowledge database
is:
̶ a tool that supports scientific research, and one of the central directions of practical informatics development
[4, 5];
̶ a development of knowledge system for RSR, for purposes of new knowledge gain (or arrangement of existing
knowledge, error checking and checking for contradictions etc.) [6–9];
̶ one of the main subsystems for the modern system of research design [10], automated workplace for RSR [4];
̶ one of the main elements for creation of permanent canonical knowledge [11] and support for knowledge ori-
ented information system functioning [12].
It is a common knowledge, that there is a tight connection between Semantic Web and UML technologies. In
particular, it is a connection between OWL syntax and visual modeling of UML-diagrams. UML language presented as
a general purpose language of visual modeling, which developed for specification, visualization, designing and docu-
menting of software components, business processes and other systems. UML language is easy and powerful tool for
modeling, which can be used effectively for creation of conceptual, logical and graphical models of complex systems
that is built for different purposes. This language absorbed all the best software engineering methods and qualities,
successfully used during many years, for modeling of large and complex systems [13].
Visual modeling in UML is possible to present as a process of gradual descent from most general and abstract
conceptual model of source system to logical, and later to physical model of respective software system. For this pur-
poses model in a form of so-called use case diagram built first. This diagram describes functional purpose of the system,
what this system will perform in a process of its functioning. Use case diagram is a source conceptual presentation, or
conceptual model of the system in the process of its designing and development [14, 15].
OS “Database of scientific publications” made for an author, who actively occupied by preparation and produc-
tion of new SP. Of course, searching through your own SP can be done manually (which in most cases exactly how it’s
done), but with the help of OS this search can be accelerated significantly. In addition, it is possible to automatically
structure received data into appropriate templates for future SP.
Now we will discuss development of architectural, structural components and UML-diagrams. Diagrams that
show OS functioning on the base of remote Apache Jena Fuseki endpoint. In addition, we will discuss examples of the
formal description of scientific paper usage by performing a set of queries.
The goal of an article – OS development. System allows significant acceleration of information retrieval by an
author (from his own DB of SP), gives visual presentation of SP concepts and respective subject area, and implements
famous Brooks formula for acquiring new knowledge [7, 8]:
( ) ( ) ,K S dI K S dS+ = +
where ( )K S – source knowledge structure, which is modified by results of information portion dI processing, cre-
ating new structure ( )K S dS+ and new knowledge portion dS . It is assumed, that components dI and dS
closely tied with elementary senses, introduced at [1].
Main stage of user tasks performance split into three OS architecture variations – A, B and C. These variations
have different functional power. A – Least powerful (organized as a local endpoint on users PC). B – Average power
(organized as a remote endpoint based on Apache Jena Fuseki server). C – most powerful (organized as a remote end-
point, which implemented with the help of original software). We can see that variations of OS realizations fit for dif-
ferent purposes. A – For one user in local network with knowledge engineer (KE), in this scenario user can form que-
Figure 1. Generalized diagram of OS DBSP development
It is a common knowledge, that there is a tight connection between Semantic Web and UML technologies. In
particular, it is a connection between OWL syntax and visual modeling of UML-diagrams. UML language presented
as a general purpose language of visual modeling, which developed for specification, visualization, designing and
documenting of software components, business processes and other systems. UML language is easy and powerful
tool for modeling, which can be used effectively for creation of conceptual, logical and graphical models of complex
systems that is built for different purposes. This language absorbed all the best software engineering methods and
qualities, successfully used during many years, for modeling of large and complex systems [13].
Visual modeling in UML is possible to present as a process of gradual descent from most general and abstract
conceptual model of source system to logical, and later to physical model of respective software system. For this
purposes model in a form of so-called use case diagram built first. This diagram describes functional purpose of the
system, what this system will perform in a process of its functioning. Use case diagram is a source conceptual presenta-
tion, or conceptual model of the system in the process of its designing and development [14, 15].
OS “Database of scientific publications” made for an author, who actively occupied by preparation and pro-
duction of new SP. Of course, searching through your own SP can be done manually (which in most cases exactly how
it’s done), but with the help of OS this search can be accelerated significantly. In addition, it is possible to automatically
structure received data into appropriate templates for future SP.
Now we will discuss development of architectural, structural components and UML-diagrams. Diagrams that
show OS functioning on the base of remote Apache Jena Fuseki endpoint. In addition, we will discuss examples of the
formal description of scientific paper usage by performing a set of queries.
The goal of an article – OS development. System allows significant acceleration of information retrieval by an
author (from his own DB of SP), gives visual presentation of SP concepts and respective subject area, and implements
famous Brooks formula for acquiring new knowledge [7, 8]:
Моделі і засоби систем баз даних та знань
Preparation stage
RDF storage of SP creation
with help of Protégé tools.
Files {SPn.owl}, n=(1,N)
Main stage.
Variation А.1
Local endpoint based on
SPARQL processor ARQ
Main stage.
Variation А.2
Local endpoint based on
The Protégé SPARQL processor
Main stage.
Variation В
Remote endpoint based on Apache Jena Fuseki
server.
Phase 1. SPARQL user queries execution.
Phase 2. Multimedia visualization of results
for user queries.
Phase 3. Manipulation by elementary senses
with purpose of gaining new knowledge
Main stage.
Variation С
Arbitral remote endpoint on the Internet
Figure 1. Generalized diagram of OS DBSP development
We know about personified knowledge database of researcher, in which a sum of functional capabilities
declared, this capabilities support processes of scientific and creative activity [2]. Such personified knowledge database
is:
̶ a tool that supports scientific research, and one of the central directions of practical informatics development
[4, 5];
̶ a development of knowledge system for RSR, for purposes of new knowledge gain (or arrangement of existing
knowledge, error checking and checking for contradictions etc.) [6–9];
̶ one of the main subsystems for the modern system of research design [10], automated workplace for RSR [4];
̶ one of the main elements for creation of permanent canonical knowledge [11] and support for knowledge ori-
ented information system functioning [12].
It is a common knowledge, that there is a tight connection between Semantic Web and UML technologies. In
particular, it is a connection between OWL syntax and visual modeling of UML-diagrams. UML language presented as
a general purpose language of visual modeling, which developed for specification, visualization, designing and docu-
menting of software components, business processes and other systems. UML language is easy and powerful tool for
modeling, which can be used effectively for creation of conceptual, logical and graphical models of complex systems
that is built for different purposes. This language absorbed all the best software engineering methods and qualities,
successfully used during many years, for modeling of large and complex systems [13].
Visual modeling in UML is possible to present as a process of gradual descent from most general and abstract
conceptual model of source system to logical, and later to physical model of respective software system. For this pur-
poses model in a form of so-called use case diagram built first. This diagram describes functional purpose of the system,
what this system will perform in a process of its functioning. Use case diagram is a source conceptual presentation, or
conceptual model of the system in the process of its designing and development [14, 15].
OS “Database of scientific publications” made for an author, who actively occupied by preparation and produc-
tion of new SP. Of course, searching through your own SP can be done manually (which in most cases exactly how it’s
done), but with the help of OS this search can be accelerated significantly. In addition, it is possible to automatically
structure received data into appropriate templates for future SP.
Now we will discuss development of architectural, structural components and UML-diagrams. Diagrams that
show OS functioning on the base of remote Apache Jena Fuseki endpoint. In addition, we will discuss examples of the
formal description of scientific paper usage by performing a set of queries.
The goal of an article – OS development. System allows significant acceleration of information retrieval by an
author (from his own DB of SP), gives visual presentation of SP concepts and respective subject area, and implements
famous Brooks formula for acquiring new knowledge [7, 8]:
( ) ( ) ,K S dI K S dS+ = +
where ( )K S – source knowledge structure, which is modified by results of information portion dI processing, cre-
ating new structure ( )K S dS+ and new knowledge portion dS . It is assumed, that components dI and dS
closely tied with elementary senses, introduced at [1].
Main stage of user tasks performance split into three OS architecture variations – A, B and C. These variations
have different functional power. A – Least powerful (organized as a local endpoint on users PC). B – Average power
(organized as a remote endpoint based on Apache Jena Fuseki server). C – most powerful (organized as a remote end-
point, which implemented with the help of original software). We can see that variations of OS realizations fit for dif-
ferent purposes. A – For one user in local network with knowledge engineer (KE), in this scenario user can form que-
where
Моделі і засоби систем баз даних та знань
Preparation stage
RDF storage of SP creation
with help of Protégé tools.
Files {SPn.owl}, n=(1,N)
Main stage.
Variation А.1
Local endpoint based on
SPARQL processor ARQ
Main stage.
Variation А.2
Local endpoint based on
The Protégé SPARQL processor
Main stage.
Variation В
Remote endpoint based on Apache Jena Fuseki
server.
Phase 1. SPARQL user queries execution.
Phase 2. Multimedia visualization of results
for user queries.
Phase 3. Manipulation by elementary senses
with purpose of gaining new knowledge
Main stage.
Variation С
Arbitral remote endpoint on the Internet
Figure 1. Generalized diagram of OS DBSP development
We know about personified knowledge database of researcher, in which a sum of functional capabilities
declared, this capabilities support processes of scientific and creative activity [2]. Such personified knowledge database
is:
̶ a tool that supports scientific research, and one of the central directions of practical informatics development
[4, 5];
̶ a development of knowledge system for RSR, for purposes of new knowledge gain (or arrangement of existing
knowledge, error checking and checking for contradictions etc.) [6–9];
̶ one of the main subsystems for the modern system of research design [10], automated workplace for RSR [4];
̶ one of the main elements for creation of permanent canonical knowledge [11] and support for knowledge ori-
ented information system functioning [12].
It is a common knowledge, that there is a tight connection between Semantic Web and UML technologies. In
particular, it is a connection between OWL syntax and visual modeling of UML-diagrams. UML language presented as
a general purpose language of visual modeling, which developed for specification, visualization, designing and docu-
menting of software components, business processes and other systems. UML language is easy and powerful tool for
modeling, which can be used effectively for creation of conceptual, logical and graphical models of complex systems
that is built for different purposes. This language absorbed all the best software engineering methods and qualities,
successfully used during many years, for modeling of large and complex systems [13].
Visual modeling in UML is possible to present as a process of gradual descent from most general and abstract
conceptual model of source system to logical, and later to physical model of respective software system. For this pur-
poses model in a form of so-called use case diagram built first. This diagram describes functional purpose of the system,
what this system will perform in a process of its functioning. Use case diagram is a source conceptual presentation, or
conceptual model of the system in the process of its designing and development [14, 15].
OS “Database of scientific publications” made for an author, who actively occupied by preparation and produc-
tion of new SP. Of course, searching through your own SP can be done manually (which in most cases exactly how it’s
done), but with the help of OS this search can be accelerated significantly. In addition, it is possible to automatically
structure received data into appropriate templates for future SP.
Now we will discuss development of architectural, structural components and UML-diagrams. Diagrams that
show OS functioning on the base of remote Apache Jena Fuseki endpoint. In addition, we will discuss examples of the
formal description of scientific paper usage by performing a set of queries.
The goal of an article – OS development. System allows significant acceleration of information retrieval by an
author (from his own DB of SP), gives visual presentation of SP concepts and respective subject area, and implements
famous Brooks formula for acquiring new knowledge [7, 8]:
( ) ( ) ,K S dI K S dS+ = +
where ( )K S – source knowledge structure, which is modified by results of information portion dI processing, cre-
ating new structure ( )K S dS+ and new knowledge portion dS . It is assumed, that components dI and dS
closely tied with elementary senses, introduced at [1].
Main stage of user tasks performance split into three OS architecture variations – A, B and C. These variations
have different functional power. A – Least powerful (organized as a local endpoint on users PC). B – Average power
(organized as a remote endpoint based on Apache Jena Fuseki server). C – most powerful (organized as a remote end-
point, which implemented with the help of original software). We can see that variations of OS realizations fit for dif-
ferent purposes. A – For one user in local network with knowledge engineer (KE), in this scenario user can form que-
– source knowledge structure, which is modified by results of information portion dI processing, creat-
ing new structure K(S + dS) and new knowledge portion dS. It is assumed, that components dI and dS closely tied with
elementary senses, introduced at [1].
Main stage of user tasks performance split into three OS architecture variations – A, B and C. These variations
have different functional power. A – Least powerful (organized as a local endpoint on users PC). B – Average power
(organized as a remote endpoint based on Apache Jena Fuseki server). C – most powerful (organized as a remote
endpoint, which implemented with the help of original software). We can see that variations of OS realizations fit for
different purposes. A – For one user in local network with knowledge engineer (KE), in this scenario user can form
queries, and receive answers only working with one science publication at a time. B – for a few users of the same sci-
163
Моделі і засоби систем баз даних та знань
entific unit. C – for users from the whole institute. For B variation it is already possible to form one query for retrieval
of structured information from multiple articles simultaneously, which is impossible to do with popular search systems.
In this material main attention will be on the description of processes with UML-diagrams usage for variation
B, phase 1 (B1).
Architectural and structural organization of OS DBSP (variation B, phase 1)
For this variation, OS functions as remote endpoint based on Apache Jena Fuseki, and consists of three phases:
phase 1 – SPARQL user queries processing; phase 2 – multimedia visualization of user query results, or creation and
usage of conceptual and figurative structures for subject area; phase 3 – manipulation by elementary senses with pur-
pose of gaining new knowledge.
At Figure 2 diagram for OS variation B1 presented.
Initially knowledge engineer downloads respective files and deploys Apache Jena Fuseki as remote endpoint [16, 17].
Than he uploads scientific publications in a form of RDF graphs to the server, this data generated on preparation stage.
Моделі і засоби систем баз даних та знань
[Введите текст]
ries, and receive answers only working with one science publication at a time. B – for a few users of the same scientific
unit. C – for users from the whole institute. For B variation it is already possible to form one query for retrieval of struc-
tured information from multiple articles simultaneously, which is impossible to do with popular search systems.
In this material main attention will be on the description of processes with UML-diagrams usage for variation B,
phase 1 (B1).
Architectural and structural organization of OS DBSP (variation B, phase 1)
For this variation, OS functions as remote endpoint based on Apache Jena Fuseki, and consists of three phases:
phase 1 – SPARQL user queries processing; phase 2 – multimedia visualization of user query results, or creation and
usage of conceptual and figurative structures for subject area; phase 3 – manipulation by elementary senses with pur-
pose of gaining new knowledge.
At Figure 2 diagram for OS variation B1 presented.
Initially knowledge engineer downloads respective files and deploys Apache Jena Fuseki as remote endpoint
[16, 17]. Than he uploads scientific publications in a form of RDF graphs to the server, this data generated on prepara-
tion stage.
User (PC)
Apache Jena Fuseki server
End-point
Sending of results to client
Execution of SPARQL queries
Data in HTTP format
Local network
Knowledge engineer (PC)
User interface
SPARQL query creation
List of queries on NL,
and visualization of results
Formatting of user query results
Data conversion module
Scientific publications
repository
(RDF graphs)
Figure 2. Generalized diagram of OS DBSP
User in his interface can see the list of possible queries on natural language. He can choose any query from this
list one by one, chosen query transferred via network to knowledge engineer module, systematically user clarifies in-
formation that he is working with. It is possible to choose a subset of articles, which used for a search, this feature is
useful if you do not need to search in all database.
Below you can see examples of queries on natural language (NL).
Basic user queries
Researcher database contains N scientific papers published in popular scientific journals. Serial numbers N of
scientific publications can be describes as follows:
N = 1, 2, …, m1, …, m2, …, mk, …, N-1, N
Serial numbers of scientific publications (in this case we deal with articles) serve as arguments for queries. Data
organized in such a way that author of SP is the first co-author in publication, or in other case, the one who owns the
database is an author.
1 Show titles of articles on the topic of “transdisciplinarity”.
2 Show titles of articles on the topic of “ontological”.
3 Show annotations of articles m1, …, m2, …, mk, …
4 Show keywords of articles m1, …, m2, …, mk, …
5 Show titles of all N articles:
5.1 in the order of publication date;
5.2 without co-authors.
…
13 Show titles of articles m1, …, m2, …, mk, …, where m1, m2, mk – query arguments set by a user.
14 Show full names of co-authors for articles m1, …, m2, …, mk, …
…
Figure 2. Generalized diagram of OS DBSP
User in his interface can see the list of possible queries on natural language. He can choose any query from
this list one by one, chosen query transferred via network to knowledge engineer module, systematically user clarifies
information that he is working with. It is possible to choose a subset of articles, which used for a search, this feature is
useful if you do not need to search in all database.
Below you can see examples of queries on natural language (NL).
Basic user queries
Researcher database contains N scientific papers published in popular scientific journals. Serial numbers N of
scientific publications can be describes as follows:
N = 1, 2, …, m1, …, m2, …, mk, …, N-1, N
Serial numbers of scientific publications (in this case we deal with articles) serve as arguments for queries.
Data organized in such a way that author of SP is the first co-author in publication, or in other case, the one who owns
the database is an author.
1 Show titles of articles on the topic of “transdisciplinarity”.
2 Show titles of articles on the topic of “ontological”.
3 Show annotations of articles m1, …, m2, …, mk, …
4 Show keywords of articles m1, …, m2, …, mk, …
5 Show titles of all N articles:
5.1 in the order of publication date;
5.2 without co-authors.
…
13 Show titles of articles m1, …, m2, …, mk, …, where m1, m2, mk – query arguments set by a user.
14 Show full names of co-authors for articles m1, …, m2, …, mk, …
…
164
Моделі і засоби систем баз даних та знань
UML-diagrams of the OS functioning for variation B1.
Now let us discuss UML-diagrams, which reveal the core of OS functions for variation B1.
On Figure 3 use case diagram presented, on Figure. 4 – class diagram, on Figure 5 – components diagram, on
Figure 6 – sequence diagram.
Моделі і засоби систем баз даних та знань
UML-diagrams of the OS functioning for variation B1.
Now let us discuss UML-diagrams, which reveal the core of OS functions for variation B1.
On Figure 3 use case diagram presented, on Figure. 4 – class diagram, on Figure 5 – components diagram, on
Figure 6 – sequence diagram.
“include”
Visualize and present
results of SPARQL query
Receive answer to a NL
query
RDF graphs repository
(of SP)
User
Form NL query and its
arguments
Transfer queries to KE
module
“extend”
Create SPARQL queryTransfer queries
to endpoint
Knowledge
engineer
Execute SPARQL query
Interface
End-point
Local network
User module
KE module
“include”
“include”
Figure 3. Use cases diagram of OS DBSP
To local network (LAN), which administrated by knowledge engineer, certain number of researchers connected.
We will discuss network operation for one user, for other users process organized in a same way.
On researchers personal computer (PC) functions module of general interface. In the interface all the queries on
NL displayed, from which researcher can choose one with desired arguments, another element of the interface shows
results of a query execution.
Other part of system contains knowledge engineer module. In this module, SPARQL-query formed out of NL-
query, and transferred over HTTP protocol to end-point. On the Apache Jena Fuseki server, SPARQL-query executed
and response sent via HTTP protocol to knowledge engineer module and respective interface.
User interface
subsystem(module)
-List of queris on natural language
+Choose query and its arguments
+Show natural language queries
+Show query results
Knowledge engineer
subsystem(module)
-List of SPARQL queries
+Translate NL query into
SPARQL query
+Send query to Apache Jena
Fuseki
+Receive data and perform
additional data conversion
End point
Apache Jena Fuseki
+Execute SPARQL query
+Show server management
interface
+Add data to dabatabase
+Remore data from database
Data storage subsystem
-RDF database
+data addition
+data removal
Figure 4. Class diagram OS DBSP
Figure 3. Use cases diagram of OS DBSP
To local network (LAN), which administrated by knowledge engineer, certain number of researchers con-
nected. We will discuss network operation for one user, for other users process organized in a same way.
On researchers personal computer (PC) functions module of general interface. In the interface all the queries
on NL displayed, from which researcher can choose one with desired arguments, another element of the interface
shows results of a query execution.
Other part of system contains knowledge engineer module. In this module, SPARQL-query formed out of NL-
query, and transferred over HTTP protocol to end-point. On the Apache Jena Fuseki server, SPARQL-query executed
and response sent via HTTP protocol to knowledge engineer module and respective interface.
Моделі і засоби систем баз даних та знань
UML-diagrams of the OS functioning for variation B1.
Now let us discuss UML-diagrams, which reveal the core of OS functions for variation B1.
On Figure 3 use case diagram presented, on Figure. 4 – class diagram, on Figure 5 – components diagram, on
Figure 6 – sequence diagram.
“include”
Visualize and present
results of SPARQL query
Receive answer to a NL
query
RDF graphs repository
(of SP)
User
Form NL query and its
arguments
Transfer queries to KE
module
“extend”
Create SPARQL queryTransfer queries
to endpoint
Knowledge
engineer
Execute SPARQL query
Interface
End-point
Local network
User module
KE module
“include”
“include”
Figure 3. Use cases diagram of OS DBSP
To local network (LAN), which administrated by knowledge engineer, certain number of researchers connected.
We will discuss network operation for one user, for other users process organized in a same way.
On researchers personal computer (PC) functions module of general interface. In the interface all the queries on
NL displayed, from which researcher can choose one with desired arguments, another element of the interface shows
results of a query execution.
Other part of system contains knowledge engineer module. In this module, SPARQL-query formed out of NL-
query, and transferred over HTTP protocol to end-point. On the Apache Jena Fuseki server, SPARQL-query executed
and response sent via HTTP protocol to knowledge engineer module and respective interface.
User interface
subsystem(module)
-List of queris on natural language
+Choose query and its arguments
+Show natural language queries
+Show query results
Knowledge engineer
subsystem(module)
-List of SPARQL queries
+Translate NL query into
SPARQL query
+Send query to Apache Jena
Fuseki
+Receive data and perform
additional data conversion
End point
Apache Jena Fuseki
+Execute SPARQL query
+Show server management
interface
+Add data to dabatabase
+Remore data from database
Data storage subsystem
-RDF database
+data addition
+data removal
Figure 4. Class diagram OS DBSP
Figure 4. Class diagram OS DBSP
165
Моделі і засоби систем баз даних та знаньМоделі і засоби систем баз даних та знань
[Введите текст]
Graphical data presentation
module
NL queries to SPARLQ converter
Connection to Apache Jena Fuseki
module
Server interface management module
Apache Jena Fuseki server
SPARQL queries execution module
Database module
User
Knowledge
engineer
depends
Processing of user
queries and
connection to Fuseki
SPARQL queries execution,
server management, data
storage
Researcher interface
module
Choose NL language query
and its argumetns
Receive results
Knowledge engineer module
depends
Figure 5. Components diagram OS DBSP
User interface
User
Show NL language
queries
Choose query and its
arguments for execution
Query conversion and
server communication
subsystem
Apche Jena
Fuseki server
Apache Jena
Fuseki database
Convert NL query into
SPARQL query
NL queries
Send SPARQL query to
server
Execute
SPARQL query
Read data from
database
Transfer data
(query execution result)
Transfer converted
data
Show results of query
execution
Figure 6. Sequence diagram OS DBSP
Operation of forming and processing for user queries, and receiving replies shown in details on main UML-
diagrams at fig.3–fig6.
Examples of SPARQL-queries execution and their results.
It is important to note that diagrams do not show process of argument selection and their transformation into ar-
ticle numbers in database.
NL-query.
Show titles of articles on the topic of “transdisciplinarity”.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
Figure 5. Components diagram OS DBSP
Моделі і засоби систем баз даних та знань
[Введите текст]
Graphical data presentation
module
NL queries to SPARLQ converter
Connection to Apache Jena Fuseki
module
Server interface management module
Apache Jena Fuseki server
SPARQL queries execution module
Database module
User
Knowledge
engineer
depends
Processing of user
queries and
connection to Fuseki
SPARQL queries execution,
server management, data
storage
Researcher interface
module
Choose NL language query
and its argumetns
Receive results
Knowledge engineer module
depends
Figure 5. Components diagram OS DBSP
User interface
User
Show NL language
queries
Choose query and its
arguments for execution
Query conversion and
server communication
subsystem
Apche Jena
Fuseki server
Apache Jena
Fuseki database
Convert NL query into
SPARQL query
NL queries
Send SPARQL query to
server
Execute
SPARQL query
Read data from
database
Transfer data
(query execution result)
Transfer converted
data
Show results of query
execution
Figure 6. Sequence diagram OS DBSP
Operation of forming and processing for user queries, and receiving replies shown in details on main UML-
diagrams at fig.3–fig6.
Examples of SPARQL-queries execution and their results.
It is important to note that diagrams do not show process of argument selection and their transformation into ar-
ticle numbers in database.
NL-query.
Show titles of articles on the topic of “transdisciplinarity”.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
Figure 6. Sequence diagram OS DBSP
Operation of forming and processing for user queries, and receiving replies shown in details on main UML-
diagrams at fig.3–fig6.
166
Моделі і засоби систем баз даних та знань
Examples of SPARQL-queries execution and their results.
It is important to note that diagrams do not show process of argument selection and their transformation into
article numbers in database.
NL-query.
Show titles of articles on the topic of “transdisciplinarity”.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
SELECT ?номер_статті ?назва_статті
{
GRAPH ?номер_статті {?s1 :Название_статьи ?назва_статті.
FILTER REGEX(?назва_статті, «трансдисципл», «i»)}
}
Query results.
Номер_статті Назва_статті
1 <http://test.ulif.org.ua:51089/articles/data/article1>
«Методологические основы развития,
становления и IT-поддержки
трансдисциплинарных исследований»
2 <http://test.ulif.org.ua:51089/articles/data/article2> «Трансдисциплинарность, информатика
и развитие современной цивилизации»
3 <http://test.ulif.org.ua:51089/articles/data/article6> «Проблемы трансдисциплинарности
и роль информатики»
4 <http://test.ulif.org.ua:51089/articles/data/article7> «Введение в класс трансдисциплинарных онтолого-
управляемых систем исследовательского проектирования»
NL-query.
Show titles of articles on the topic of “ontological”.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
SELECT DISTINCT ?номер_статті ?назва_статті
{
GRAPH ?номер_статті {?s1 :Название_статьи ?назва_статті.
FILTER REGEX(?назва_статті, «онтолог», «i»)}
}
Query results.
Номер_статті Назва_статті
1 <http://test.ulif.org.ua:51089/articles/data/article5> «Про деякі особливості побудови онтологічних моделей
предметних областей»
2 <http://test.ulif.org.ua:51089/articles/data/article7> «Введение в класс трансдисциплинарных онтолого-
управляемых систем исследовательского проектирования»
3 <http://test.ulif.org.ua:51089/articles/data/article8> «Онтологическая концепция информатизации научных
исследований»
4 <http://test.ulif.org.ua:51089/articles/data/article10> «Архитектура онтолого-управляемых компьютерных систем»
5 <http://test.ulif.org.ua:51089/articles/data/article16> «К вопросу системно-онтологической интеграции знаний
предметной области»
6 <http://test.ulif.org.ua:51089/articles/data/article19>
«Знание-ориентированные информационные системы с
обработкой естественно-языковых объектов: онтологиче-
ский подход»
7 <http://test.ulif.org.ua:51089/articles/data/article21> «Системно-онтологический анализ предметной области»
NL-query.
Show annotations of articles 1, 2, 7.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
SELECT ?номер_статті ?назва_статті (group_concat(?анотація) as ?анотація_повна)
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article1>
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article2>
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article7>
{
167
Моделі і засоби систем баз даних та знань
GRAPH ?номер_статті {?s1 :Название_статьи ?назва_статті.
{:Аннотация :Иметь_Предложение ?речення}
{?речення :Иметь_Текст ?анотація}
}
}
group by ?номер_статті ?назва_статті
Query results.
Номер_статті Назва_статті Анотація_повна
1 <http://test.ulif.org.ua:51089/articles/data/
article1>
«Методологические
основы развития, станов-
ления и IT-поддержки
трансдисциплинарных
исследований»
«Разработаны основы методо-
логии трансдисциплинарного
системного подхода к постанов-
ке и выполнению научных ис-
следований и сложных приклад-
ных проектов с акцентом на их
IT-поддержку с использованием
методов и средств искусственно-
го интеллекта, в частности онто-
логического инжиниринга. …
2 <http://test.ulif.org.ua:51089/articles/data/
article2>
«Трансдисциплинар-
ность, информатика и
развитие современной
цивилизации»
«Перспективы и проблемы раз-
вития человеческой цивилизации
всегда волновали общество. …
3 <http://test.ulif.org.ua:51089/articles/data/
article7>
«Введение в класс транс-
дисциплинарных онтоло-
го-управляемых систем
исследовательского про-
ектирования»
«Рассмотрен класс систем иссле-
довательского проектирования,
основанных на использовании
парадигм трансдисциплинарно-
сти, онтологического управления
и целенаправленного развития.
…
NL-query.
Show keywords of articles 1, 2, 7.
SPARQL-query.
PREFIX : <http://www.semanticweb.org/николай/ontologies/2020/5/19/untitled-ontology-36#>
SELECT ?номер_статті ?назва_статті ?ключові_слова
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article1>
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article2>
FROM NAMED <http://test.ulif.org.ua:51089/articles/data/article7>
{
GRAPH ?номер_статті { ?s1 :Название_КС ?ключові_слова OPTIONAL
{?s2 :Название_статьи ?назва_статті}}
}
Query results.
Номер_статті Назва_статті Ключові_слова
1 <http://test.ulif.org.ua:51089/articles/data/article7>
«Введение в класс
трансдисциплинарных
онтолого-управляемых
систем исследователь-
ского проектирования»
«трансдисциплинарность, онто-
логическое управление, вирту-
альные структуры (парадигма),
развивающиеся системы, ноос-
ферогенез, ноосфера, научная
картина мира, трансдисципли-
нарный подход (знания), кла-
стеры конвергенции, онтологи-
ческий подход, онтологическая
концепция, формальная онтоло-
гия, формула Брукса, интеллек-
туальные ИС, трансдисципли-
нарные онтолого-управляемые
ИС, исследовательское проек-
тирование, персональные базы
знаний, предметная область,
GRID-сети»
168
Моделі і засоби систем баз даних та знань
2 <http://test.ulif.org.ua:51089/articles/data/article2>
«Трансдисциплинар-
ность, информатика и
развитие современной
цивилизации»
«трансдисциплинарность,
информатика, мониторинг,
кластер конвергенции,
компьютерная онтология,
knowledge engineering, Еди-
ная национальная сеть ин-
форматизации, глобальная
сеть трансдисциплинарных
знаний.
3 <http://test.ulif.org.ua:51089/articles/data/article1>
«Методологические ос-
новы развития, станов-
ления и IT-поддержки
трансдисциплинарных
исследований»
«научная картина мира, ин-
формационная технология,
развивающаяся информацион-
ная система, трансдисципли-
нарность, трансдисциплинар-
ные исследования, трансдис-
циплинарные знания, кластер
конвергенции, онтология,
онтологическая концепция,
онтолого-ориентиро-ванная
поддержка.»
Conclusion
The goal of our research was to develop an ontological system for processing of databases of scientific pub-
lications, which will allow a researcher to increase significantly retrieval speed of required information (in from of
cognitive structures) from his own sources.
In this article was introduced and described architectural and structural organization of OS, which includes lo-
cal network with PCs of user and administrator/knowledge engineer, and remote endpoint based on Apache Jena Fuseki
server, was shown main UML-diagrams of OS functioning, and examples of user queries execution.
Further research
This research is far from its end goal. As we explained, it is necessary to implement phases 2 and 3, for
that we need to develop algorithms of creation for conceptual and figurative structures, algorithms of their com-
parison and analysis with further intention of building subject area knowledge, and algorithms for discovery of a
new knowledge in accordance with Brooks formula.
In the future research, our team will develop original instruments and tools with purpose of optimization for
user queries, and optimization of usability for ontology system.
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Література
1. Палагін О.В., Петренко М.Г. Знання-орієнтований інструментальний комплекс обробки баз даних наукових публікацій. Con-
trol systems and computers. 2020. №5. С. 17–33. URL: https://doi.org/10.15407/usim.2020.05.003 (дата звернення: 23.06.2022).
2. Palagin A.V., Petrenko N.G., Velychko V.Yu., Malakhov K.S., 2014. Development of formal models, algorithms, procedures, engi-
neering and functioning of the software system “Instrumental complex for ontological engineering purpose”. In: Proceedings of the
9th International Conference of Programming UkrPROG. CEUR Workshop Proceedings 1843. Kyiv, Ukraine, May 20-22, 2014. URL:
http://ceur-ws.org/Vol-1843/221-232.pdf (дата звернення: 23.06.2022).
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Received 19.07.2022
About the authors:
Oleksandr Palagin,
Doctor of Sciences,
Academician of National Academy of Sciences of Ukraine,
Deputy director of Glushkov Institute of Cybernetics,
head of department 205 at Glushkov Institute of Cybernetics,
590 Ukrainian publications,
101 International publications,
H-index: Google Scholar – 20,
Scopus – 9,
http://orcid.org/0000-0003-3223-1391.
170
Моделі і засоби систем баз даних та знань
Mykola Petrenko,
Doctor of Sciences, Leading researcher,
98 Ukrainian publications,
15 International publications,
H-index: Google Scholar – 10,
Scopus – 2,
http://orcid.org/0000-0001-6440-0706.
Mykola Boyko,
Research Fellow,
http://orcid.org/0000-0003-1723-5765.
Place of work:
Glushkov Institute of Cybernetics of
National Academy of Sciences of Ukraine,
40 Glushkov ave., Kyiv, Ukraine, 03187,
tel.: (+38) (044) 526 3348
email: palagin_a@ukr.net
Прізвища та ініціали авторів і назва доповіді англійською мовою:
O.V. Palagin, M.G. Petrenko, M.O. Boyko
Ontological system processing of databases of scientific publications
Прізвища та ініціали авторів і назва доповіді українською мовою:
О.В. Палагін, М.Г. Петренко, М.О. Бойко
Онтологічна система оброблення баз даних наукових публікацій
|
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| language | English |
| last_indexed | 2025-07-17T09:41:07Z |
| publishDate | 2023 |
| publisher | PROBLEMS IN PROGRAMMING |
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| spelling | pp_isofts_kiev_ua-article-5182023-06-25T05:35:18Z Ontological system processing of databases of scientific publications Онтологічна система оброблення баз даних наукових публікацій Palagin, O.V. Petrenko, N.G. Boyko, M.O. transdisciplinary scientific research; semantic web technology; ontological engineering; scientific publications database UDC 004.318 трансдисциплінарні наукові дослідження; технології semantic web; онтологічний інжиніринг; база даних наукових публікацій УДК 004.318 Development of theories, methods and algorithms for the discovery and formation of new knowledge always was one of the most important tasks for any researcher, especially if they actively working on creation of new scientific publications. There is no universal language to describe formally concepts (knowledge) and systemology of transdisciplinary scientific research. Because of this, researchers have a set of urgent problems, and one of them is the way of speeding up the process of finding information (in the form of cognitive-structure) in their own sources. Ontological system for processing of databases of scientific publications created to solve this problem for a researcher, who have from tens to hundreds of scientific papers published. We are unaware of search systems, which would provide the same information for a researcher in such a short time. Ontological system implements technologies of Information Retrieval and Knowledge Discovery in Databases with accent on technologies and instruments such as Semantic Web and cognitive graphics. Development of such ontological system have three stages. On the first stage instruments for system development created, methods and algorithms of interaction between system components "User ¾ Knowledge engineer ¾ Remote endpoint", also data added to the system at this stage. On the second stage task of multimedia presentation for conceptual and figurative structures, described in scientific documents, solved. Gaining new knowl- edge problem solved on the third stage.Prombles in programming 2022; 3-4: 161-170 Розроблення теорій, методів й алгоритмів виявлення та формування нових знань завжди займало одне з центральних місць у будь-якого наукового співробітника, тим паче якщо він активно працює над створенням нових наукових публікацій. Відомо, що універсальної мови формального опису концептів (знань) та системології трансдисциплінарних наукових досліджень не існує. А тому перед науковцями стоїть ряд першочергових проблем, в тому числі проблема значного пришвидшення отримання науковим співробітником необхідної йому когнітивно-структурованої інформації із своїх джерел. Онтологічна система оброблення баз даних наукових публікацій саме так орієнтована на наукового співробітника, у якого в наявності опубліковано від декількох десятків до сотень наукових праць. Нам невідомі пошукові системи, які змогли б у максимально стислий термін надати науковому співробітнику таку інформацію. Онтологічна система реалізує технології Information Retrieval і Knowledge Discovery in Databases з акцентом на технології й інструментарій Semantic Web та когнітивної графіки. Розроблення такої онтологічної системи при- пускає три стадії: на першій стадії створюються інструментальні засоби реалізації системи, методики й алгоритми взаємодії системи «Користувач ¾ Інженер зі знань ¾ Віддалена прикінцева точка» та наповнення її даними; на другій стадії вирішуються задачі мультимедійного подання образно-понятійних структур, що описані в наукових документах; і на третій стадії – вирішення проблеми добування нових знань. Prombles in programming 2022; 3-4: 161-170 PROBLEMS IN PROGRAMMING ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ ПРОБЛЕМИ ПРОГРАМУВАННЯ 2023-01-23 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/518 10.15407/pp2022.03-04.161 PROBLEMS IN PROGRAMMING; No 3-4 (2022); 161-170 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 3-4 (2022); 161-170 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 3-4 (2022); 161-170 1727-4907 10.15407/pp2022.03-04 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/518/571 Copyright (c) 2023 PROBLEMS IN PROGRAMMING |
| spellingShingle | transdisciplinary scientific research semantic web technology ontological engineering scientific publications database UDC 004.318 Palagin, O.V. Petrenko, N.G. Boyko, M.O. Ontological system processing of databases of scientific publications |
| title | Ontological system processing of databases of scientific publications |
| title_alt | Онтологічна система оброблення баз даних наукових публікацій |
| title_full | Ontological system processing of databases of scientific publications |
| title_fullStr | Ontological system processing of databases of scientific publications |
| title_full_unstemmed | Ontological system processing of databases of scientific publications |
| title_short | Ontological system processing of databases of scientific publications |
| title_sort | ontological system processing of databases of scientific publications |
| topic | transdisciplinary scientific research semantic web technology ontological engineering scientific publications database UDC 004.318 |
| topic_facet | transdisciplinary scientific research semantic web technology ontological engineering scientific publications database UDC 004.318 трансдисциплінарні наукові дослідження технології semantic web онтологічний інжиніринг база даних наукових публікацій УДК 004.318 |
| url | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/518 |
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