Developing theories, methods and algorithms to discover and generate new knowledge has always been one of the most important tasks of any researcher, especially if they are actively working on the creation of new scientific publications. There is no universal language for the formal description of concepts (knowledge) and systemology of transdisciplinary scientific research. This raises a set of topical problems for researchers, and one of them is the way to speed up information (in the form of cognitive-structure) search process in their own sources. The ontology-related complex for semantic processing of scientific data is designed to solve this problem for the researcher who has dozens to hundreds of published scientific papers. We are not aware of any search engines that could provide the same information for a researcher in such a short time. The ontology-related complex implements information retrieval and knowledge discovery technologies with emphasis on technologies and tools such as Semantic Web and cognitive graphics. The development of such a complex consists of three stages. The first stage creates the instruments for complex development, methods and algorithms for interaction between the components of the system “User Knowledge engineer Remote endpoint”, also at this stage data is added to the system. The second stage solves the task of multimedia representation for conceptual and figurative structures described in scientific documents. The third stage solves the task of acquiring new knowledge.
There are many applications for searching information from various databases (DBs), including specialized ones. Most of these applications do not take into account the cognitive aspect of data processing, which is necessary for creativity, in particular for the researcher (RSR).
A separate problem is the multimedia (conceptual and figurative) representation of search results and their comparison with the conceptual structure of the Knowledge Domain (KD); this is of interest in order to gain new knowledge. The processing of scientific publications by a single author, authors of a scientific unit or institute using Semantic Web technology is relevant for scientific research.
The ontology related complex (OrC) for semantic processing of scientific data uses information
retrieval and knowledge discovery technologies with a focus on Semantic Web and cognitive graphics
technologies and tools [
A summary chart for the development of the OrC DBSP is shown in Figure 1. It includes a
preparation stage block and main stage blocks with variations A, B, and C. The preparation stage is
described in detail in [
We are aware of personalized knowledge base of the researcher, in which a number of
functionalities declared, these features support the processes of scientific and creative activity [
There is a close relationship between Semantic Web and UML technologies. In particular, we are talking about the relationship between the Web Ontology Language (OWL) syntax and the visual modeling of Unified Modeling Language (UML) diagrams. UML is presented as a general-purpose visual modeling language, which is designed to specify, visualize, design and document components of software, business processes and other systems. UML is a simple and powerful modelling tool, that can be used effectively to create conceptual, logical and graphical models of complex systems that are built for different purposes. This language incorporates all the best practices and qualities of software engineering that have been successfully used for many years, to model large and complex systems [13].
Visual modeling in UML can be represented as a process of gradual descent from the most general and abstract conceptual model of the original system to the logical and then to the physical model of the corresponding software system. For these purposes, first a model is built in the form of a so-called use case diagram. This diagram describes the functional purpose of the system, what the system will do in the course of its operation. A use case diagram is an initial conceptual representation, or conceptual model, of a system during its design and development [14, 15].
OrC "Database of scientific publications" was created for the author, who is actively involved in the preparation and release of new SPs. Of course, it is possible to search in your own SPs manually (which happens in most cases), but with the help of the OrC this search can be greatly accelerated. In addition, it is possible to automatically structure the retrieved data into appropriate templates for future SPs.
We will now discuss the development of architectural, structural components and UML-diagrams. Diagrams showing the operation of the OrC based on the Apache Jena Fuseki remote endpoint. In addition, we will discuss examples of how to use a formally described scientific paper, and perform a number of queries to some papers.
The purpose of this paper is to show the process of the OrC development. The complex allows us to greatly accelerate the search for information by the user (in his own DB of SPs), gives visual representation of the publication concepts and the relevant subject area, and implements Brooks’ famous formula for gaining new knowledge [7, 8]: where
is the original knowledge structure, which is modified by the results of processing of the
information portion , creating a new structure and new knowledge portion . It
is assumed that the components and are closely related to the elementary meanings,
introduced in [
In [
Elementary meanings created out of the sentences of the text of the article, depending on the complexity of the sentence and its contents (in accordance with the syntax of the Ukrainian language) are formed like this: Complex sentences split into simple sentences. Simple and complicated sentences split into simple sentences that have subject, predicate and direct object. Simple two-member sentence represented as subject (S), predicate (P) and object (O). The elementary meaning is a certain equivalent of the RDF triple of the Semantic Web. On the Figure 2 and Figure 3, you can see mentioned (X, R) ontology graph, and ontology graph of scientific publication, created with the Protégé 5.5.0 instruments. This ontology graphs describe “article 1”, they are used to show examples of SPARQL queries and results too. English version of “article 1” was printed in Journal of Automation and Information Sciences, vol 50, 2018, Issue 10, PP. 1-17.
The main stage of user tasks is broken down into three variants of the OrC architecture - A, B and C. These variants have different functional capacities. A – Least powerful (organized as a local endpoint on the user PC). B – Medium power (organized as a remote endpoint based on the Apache Jena Fuseki server). C – most powerful (organized as a remote endpoint, which is implemented using the original software). We can see that different variants of the OrC are suitable for different purposes. A – For a single user in a local network with a knowledge engineer (KE), in this scenario the user can form queries and get answers, working with only one science publication at a time. B – for multiple users of the same research unit. C – for users of the whole institute. For option B it is already possible to form a single query to retrieve structured information from several articles simultaneously, which cannot be done with popular search engines.
This material will focus on describing processes using UML-diagrams for variant B, phase 1 (B1). 2. Architectural and structural organization of the OrC DBSP (variant B, phase 1)
In this variant, the OrC functions as a remote endpoint based on the Apache Jena Fuseki and consists of three phases: phase 1 – processing of user SPARQL queries; phase 2 – multimedia visualization of user query results, or creation and use of conceptual and figurative structures of the subject area; phase 3 – manipulation of elementary meanings with purpose of gaining new knowledge.
Figure 4 shows a generalized diagram for the OrC B1 variant.
First, the knowledge engineer downloads the appropriate files and deploys Apache Jena Fuseki as a remote endpoint [16, 17]. He then uploads scientific publications to the server in the form of RDF graphs; this data is generated during the preparation stage.
The user sees in his interface a list of possible natural language queries. He can select any query from this list one by one, the selected query is sent over the network to the knowledge engineer module. Systematically, the user specifies the information with which he works. It is possible to select a subset of the articles that are used for the search, this feature is useful if it is not necessary to search the entire database.
The researcher’s database contains N scientific articles published in popular scientific journals.
N = 1, 2, …, m1, …, m2, …, mk, …
The serial numbers of scientific publications (in this case we are dealing with articles) serve as arguments for queries. The data are organized so that the author of a scientific publication is the first co-author in the publication; otherwise, the owner of the database is the author.
1. Show the titles of the articles on the subject of “transdisciplinarity”. 2. Show the titles of the articles on the subject of “ontological”. 3. Show the abstracts of the articles m1, …, m2, …, mk, … 4. Show the keywords of the articles m1, …, m2, …, mk, … 5. Show the titles of all N articles: 6. Show the titles of all N articles in the order of the date of publication; 7. Show the titles of all N articles without co-authors. 8. Show the titles of the articles m1, …, m2, …, mk, …, where m1, m2, mk are user-defined query arguments.
…
A certain number of researchers are connected to a local area network (LAN), which is managed by a knowledge engineer. We will look at the operation of the system for one user, for other users the process is organized in a similar way.
A general interface module functions on the researcher’s personal computer (PC). The interface displays all queries in natural language. The researcher can select a single query with the required arguments. Another interface element shows the results of the query.
The other part of the system contains a knowledge engineer module. This module generates a SPARQL-query from a NL-query, and sends it over HTTP protocol to the end-point. The Apache Jena Fuseki server executes the SPARQL-query and sends the result back to the knowledge engineer module.
The sequence of the generation and processing of the user queries is shown in detail in Fig. 5 to Fig. 8.
Show the titles of the articles on the topic of “ontological”.
ArticleName "Про деякі особливості побудови онтологічних моделей предметних областей" "Введение в класс трансдисциплинарных онтолого-управляемых систем исследовательского проектирования" "Онтологическая концепция информатизации научных исследований" "Архитектура онтолого-управляемых компьютерных систем" "К вопросу системно-онтологической интеграции знаний предметной области" "Знание-ориентированные информационные системы с обработкой естественно-языковых объектов: онтологический подход" "Системно-онтологический анализ предметной области"
Show the annotations of the articles 1, 2, 7.
{
GRAPH ?номер_статті {?s1 :Название_статьи ?ArticleName.
{:Аннотация :Иметь_Предложение ?речення} {?речення :Иметь_Текст ?анотація} {bind(strafter(str(?номер_статті),str(:)) as ?ArticleNumber)} } } group by ?ArticleNumber ?ArticleName
ArticleName 1 2 7 "Методологические основы развития, становления и IT
поддержки трансдисциплинарных
исследований" "Трансдисциплинарность, информатика и развитие современной цивилизации"
"Введение в класс трансдисциплинарных онтолого-управляемых систем исследовательского проектирования"
Abstract "Разработаны основы методологии трансдисциплинарного системного подхода к постановке и выполнению научных исследований и сложных прикладных проектов с акцентом на их ITподдержку с использованием методов и средств искусственного интеллекта, в частности
онтологического инжиниринга. … "Перспективы и проблемы развития человеческой цивилизации всегда волновали общество. … "Рассмотрен класс систем исследовательского проектирования, основанных на использовании парадигм трансдисциплинарности, онтологического управления и целенаправленного развития. …
Show the keywords of the articles 1, 2, 7.
KeyWords "трансдисциплинарность, онтологическое управление, виртуальные структуры (парадигма),
развивающиеся системы, ноосферогенез, ноосфера, научная картина мира, трансдисциплинарный "Введение в класс подход (знания), кластеры трансдисциплинарных онтолого- конвергенции, онтологический управляемых систем подход, онтологическая концепция, исследовательского проектирования" формальная онтология, формула Брукса, интеллектуальные ИС, трансдисциплинарные онтологоуправляемые ИС, исследовательское проектирование, персональные базы знаний, предметная область, GRID
сети" "трансдисциплинарность, информатика, мониторинг, кластер "Трансдисциплинарность, конвергенции, компьютерная информатика и развитие онтология, knowledge engineering, современной цивилизации" Единая национальная сеть информатизации, глобальная сеть трансдисциплинарных знаний.
"научная картина мира, информационная технология, развивающаяся информационная "Методологические основы развития, система, трансдисциплинарность, становления и IT-поддержки трансдисциплинарные исследования, трансдисциплинарных исследований" трансдисциплинарные знания, кластер конвергенции, онтология, онтологическая концепция, онтологоориентиро-ванная поддержка."
Show the names of the chapters of the article 1.
1 1 "Методологические основы развития, становления и IT
поддержки трансдисциплинарных
исследований" "Методологические основы развития, становления и IT
поддержки трансдисциплинарных
исследований" "Методологические основы развития, становления и IT
поддержки трансдисциплинарных
исследований" "Методологические основы развития, становления и IT
поддержки трансдисциплинарных исследований" :Раздел_1 "Определения и сущность понятий, связанных с дисциплинарными ис
следованиями" "Трансдисциплинарность – :Раздел_2 ноосферная концепция – картина мира" "Основы онтолого:Раздел_3 ориентированной поддержки ТДисследований" :Раздел_4 "Информационные технологии поддержки ТД научных исследований"
The aim of our research was to develop an ontology-related complex for semantic processing of scientific data, which will allow the researcher to significantly increase the rate of extraction of necessary information (in the form of cognitive structures) from their own sources.
This paper presented and described the architectural and structural organization of the OrC, which includes a local area network of a user and knowledge engineer PCs, and a remote endpoint based on the Apache Jena Fuseki server. UML diagrams show the functioning of the OrC. Also shown are examples of user queries.
This study is still far from its final goal. As we have already explained, phases 2 and 3 need to be implemented. Algorithms for creating conceptual and figurative structures, algorithms for comparing and analyzing these structures with the further intention of constructing KD data, and algorithms for discovering new knowledge, including the algorithms according to the Brooks formula, need to be developed.
In future research, our team will develop original tools and facilities in order to optimize user queries and optimize the usability of the ontology-related complex. [4] Designing and program implementation of the subsystem for creation and use of the ontological knowledge base of the scientific employee publications. Problems in programming. 2017. №2. p. 72–81. doi.org/10.15407/pp2017.02.072. [5] A. Palagin, V. Velychko, K. Malakhov, O. Shchurov, Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach. In: Proceedings of the 12th International Scientific and Practical Conference of Programming UkrPROG 2020. CEUR Workshop Proceedings 2866. Kyiv, Ukraine, September 15-16, 2020. [Online] Available from: http://ceur-ws.org/Vol-2866/ceur_342352palagin34.pdf [Accessed: 20 June 2022]. [6] A. Palagin, N. Petrenko, (2018) Methodological Foundations for Development, Formation and IT-support of Transdisciplinary Research. Journal of Automation and Information Sciences. 50(10). p. 1-17. doi.org/10.1615/JAutomatInfScien.v50.i10.10. [7] S. Kotlyk, (Ed.) New Information Technologies, Simulation and Automation. Iowa State
[8] A. Palagin, Transdisciplinarity problems and the role of informatics. Cybernetics and Systems Analysis/ International Theoretical Science Journal. 2013, № 5 – p.3–13. doi.org/10.1007/s10559-013-9551-y. [9] A. Palagin, Architecture of ontology-driven computer systems. Cybernetics and Systems Analysis/ International Theoretical Science Journal. 2006, № 2 – p. 254–264. doi.org/10.1007/s10559-006-0061-z. [10] A. Palagin, An Introduction to the Class of the Transdisciplinary Ontology-controled Research Design Systems. Control systems and computers. 2016. № 6. p. 3–11. doi.org/10.15407/usim.2016.06.003. [11] A. Palagin, A. Kurgaev, Interdisciplinary scientific research: optimization of system and information support. (2009). Bulletin of the National Academy of Sciences of Ukraine. 2009, № 3, p.14–25. [Online] Available from: ftp://ftp.nas.gov.ua/akademperiodyka/Downloads/
[12] A. Palagin, N. Petrenko, S. Kryvyy, On the construction of knowledge-oriented computer systems for scientific research. Control systems and computers. 2015. №2. p. 64–73. [Online]
[13] G. Booch, J. Rumbaugh, I. Jacobson, The Unified Modeling Language User Guide. Reading,
MA, 2005. 475 p. [14] D. Schmuller, Teach Yourself UML in 24 Hours, Complete Starter Kit. М.: Williams, 2005. 416 p. [15] А. Leonenkov, Tutorial UML 2. St. Petersburg: BHV-Petersburg, 2007. 576 p. ISBN 978-594157-878-8. [16] https://jena.apache.org/documentation/fuseki2/(date of access: 23 June 2022). [17] B. Ducharme, Learning SPARQL. Querying and Updating with SPARQL 1.1 (Second edition).