scholarly journals The Design of Inductive Modeling Tools Using Ontologies

2020 ◽  
pp. 46-54
Author(s):  
Halyna A. Pidnebesna ◽  
Keyword(s):  
Knowledge Base ◽  
Statistical Data ◽  
Subject Area ◽  
Formal Description ◽  
Complex Objects ◽  
Process Information ◽  
Modeling Tools ◽  
Ontological Approach ◽  
New Knowledge ◽  
The Subject

The architecture of the GMDH-based inductive modeling tools is considered. A feature is the use of the knowledge base in the form of an ontology of the subject area of inductive modeling. The application of the ontological approach to the design of the knowledge base makes it possible to automatically acquire new knowledge, efficiently process information in the modeling of complex objects of different nature according to statistical data, generate queries and obtain logical inferences. Fragments of the GMDH-based inductive modeling ontology are given as an example of creating a formal description of the subject area. The Protege onto editor was used to construct ontologies.

scholarly journals AN ONTOLOGICAL APPROACH TO THE DEVELOPMENT OF A KNOWLEDGE BASE OF A DECISION SUPPORT SYSTEM FOR CHOOSING AN INDIVIDUAL EDUCATIONAL TRAJECTORY

2021 ◽  
pp. 29-34
Author(s):  
О.В. Баюк ◽  
И.О. Лозикова
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Support System ◽  
Subject Area ◽  
Educational Trajectories ◽  
Ontological Approach ◽  
The Subject ◽  
Ontological Modeling ◽  
Ontological Model

В статье рассмотрены актуальные вопросы онтологического моделирования базы знаний интеллектуальной системы поддержки принятия решения (СППР) по выбору индивидуальной образовательной траектории. Цель работы – разработка онтологической модели базы знаний системы поддержки принятия решения для построения индивидуальной образовательной траектории обучающегося. Предметом исследования является онтологическое моделирование процесса построения индивидуальной образовательной траектории. Актуальность работы обусловлена индивидуализацией образовательного процесса, который предлагается осуществлять по индивидуальным образовательным траекториям (ИОТ). При построении ИОТ большая роль отводится самостоятельному выбору и принятию решения обучающимся, где необходимо ясно видеть возможный результат своего выбора. Система построения сценариев выбора ИОТ или Система поддержки принятия решения (СППР, Decision Support System, DSS) является интеллектуальной системой, ядром которой будет база знаний на основе онтологической модели. Представлены методологические и технологические аспекты моделирования онтологии базы знаний предметной области индивидуальных образовательных траекторий, применение современных программных средств и стандартов разработки онтологических моделей. Описан метод и технология онтологического моделирования, представлены основные технологические стандарты и их теоретические основания, а также основные процессы методики проектирования. Дано описание предметной области онтологической модели, подход к построению модели базы знаний индивидуальной образовательной траектории, где сформулированы целевые вопросы данной онтологии для определения её масштаба и компетентности. Представлен последовательный процесс онтологического моделирования знаний описанной предметной области. Разработаны правила логического вывода для проверки компетентности онтологической модели и представлены результаты логических вычислений. Представлен выбор семантических технологий для разработки СППР. В заключение сделаны выводы о преимуществах онтологического подхода к разработке базы знаний системы поддержки принятия решения по выбору индивидуальной образовательной траектории и о программной архитектуре реализации данной системы. The article deals with topical issues of ontological modeling of the knowledge base of the intelligent decision support system (DSS) for the choice of an individual educational trajectory. The purpose of the work is to develop an ontological model of the knowledge base of the decision support system for building an individual educational trajectory of a student. The subject of the research is the ontological modeling of the process of building an individual educational trajectory. The relevance of the work is due to the individualization of the educational process, which is proposed to be carried out according to individual educational trajectories (IOT). When building an IOT, a large role is assigned to the independent choice and decision-making of students, where it is necessary to clearly see the possible result of their choice. The system for constructing IOT selection scenarios or the Decision Support System (DSS, Decision Support System, DSS) is an intelligent system, the core of which will be a knowledge base based on an ontological model. Methodological and technological aspects of modeling the ontology of the knowledge base of the subject area of individual educational trajectories, the use of modern software tools and standards for the development of ontological models are presented. The method and technology of ontological modeling are described, the main technical standards and their theoretical foundations are presented, as well as the main processes of the design methodology. The article describes the subject area of the ontological model, an approach to building a knowledge base model of an individual educational trajectory, where the target questions of this ontology are formulated to determine its scale and competence. A sequential process of ontological modeling of knowledge of the described subject area is presented. The rules of logical inference for checking the competence of the ontological model are developed and the results of logical calculations are presented. The choice of semantic technologies for the development of DSS is presented. In conclusion, conclusions are made about the advantages of the ontological approach to the development of the knowledge base of the decision support system for choosing an individual educational trajectory and about the software architecture of the implementation of this system.

scholarly journals USING THE ONTOLOGY FOR CREATING KNOWLEDGE BASE OF THE EXPERT SYSTEM OF THE SUBJECT AREA OF THE STORING OF VEGETABLES AND FRUITS BY THE TECHNOLOGY «ULO»

2020 ◽  
Author(s):  
Ye. Proskurka ◽  
S. Kyrychuk
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Subject Area ◽  
Automation System ◽  
The Subject ◽  
Vegetables And Fruits

Using the ontology for creating the knowledge base of the expert system of the subject area of the storing of vegetables and fruits by the technology «ULO» is reviewed in this article. The expert system, which will be created with CLIPS language, will be used for manage of the automation system of the warehouses for the storing of vegetables and fruits by the technology «ULO».

Implementation of Mathematical Modeling Tools for Analysis of Economic Processes and Forecasting of Expenses

2021 ◽  
pp. 35-40
Author(s):  
A. Kulyasova ◽  
V. Bondarev
Keyword(s):  
Mathematical Modeling ◽  
Subject Area ◽  
Mathematical Statistics ◽  
Mathematical Apparatus ◽  
Modeling Tools ◽  
Modeling Methods ◽  
The Subject

The article is devoted to the consideration of various practices of using mathematical modeling for analysis of economic processes. The article contains specific examples of the application of mathematical modeling methods, including methods based on tools of the theory of probability and mathematical statistics. The purpose of the article is to draw the reader's attention to the widespread applicability and effectiveness of methods and tools of economic and mathematical modeling for solving applied problems in the subject area. The study has shown that the use of individually adapted models based on the basic elements of the mathematical apparatus is effective for solving highly specialized applied problems including tasks connected with forecasting costs and assessing possible risks.

scholarly journals A new method for creating Mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems

2021 ◽  
Vol 2061 (1) ◽  
pp. 012123
Author(s):  
D A Chuvikov ◽  
D V Aladin ◽  
L E Adamova ◽  
O O Varlamov ◽  
V G Osipov
Keyword(s):  
Knowledge Base ◽  
Control Systems ◽  
Knowledge Bases ◽  
Subject Area ◽  
Vehicle Control ◽  
Matrix Form ◽  
Intelligent Vehicle ◽  
Knowledge Map ◽  
Knowledge Model ◽  
The Subject

Abstract This research presents a methodology for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. At its core, this methodology is kind of instruction for analysts facing the task of formalizing knowledge in a given subject area. The result of this formalization is a “knowledge map” created according to a special proposed template. In the future, this template allows forming a knowledge base for a given subject area in the formalism of bipartite oriented mivar networks. As an example, the subject area of ground-based intelligent vehicle control systems is used as a template. The proposed methodology of knowledge formalization makes it possible to simplify the process of creating models in Wi!Mi “Razumator-Consultant” 2.1 and also to level the probability of logical collisions when designing a knowledge model in the formalism of bipartite oriented mivar networks.

scholarly journals Semantic library LibMeta as a tool for building the knowledge space of a scientific subject area

2021 ◽  
Author(s):  
Olga Muratovna Ataeva ◽  
Vladimir Alekseevich Serebryakov
Keyword(s):  
Information System ◽  
Data Structure ◽  
Knowledge Base ◽  
Subject Area ◽  
Technical Knowledge ◽  
Formal Description ◽  
Knowledge Space ◽  
Scientific Subject

The paper considers an information system designed to represent a subject area related to science and its features. Highlighted general concepts for formal description of such a subject area in the knowledge base of the semantic library. The peculiarity of these areas is that the data structure is subject to frequent changes. Therefore, the means of organizing knowledge, which is a semantic library, should be sufficiently universal and not require deep technical knowledge. The paper describes the functionality of the system and its use.

scholarly journals Development of a technology for collecting and analyzing data for monitoring based on an ontological approach

2021 ◽  
Author(s):  
Yu.I. Molorodov ◽  
O.V. Kasatkin
Keyword(s):  
Real World ◽  
World System ◽  
Subject Area ◽  
Digital Representation ◽  
Digital Twin ◽  
Information Models ◽  
Hydroelectric Dam ◽  
Ontological Approach ◽  
The Subject ◽  
Ontological Modeling

One of the ways to build information models is ontological modeling. The use of ontologies greatly facilitates the exchange of data between embedded models and utilities for the digital representation of an object or a real-world system, sometimes called “digital twin” (DT). It is also important to establish a correspondence between the DT, people and external programs. Based on the dictionary of the main terms, classes, objects of the subject area and the relations between them, we have built an ontology of the hydroelectric dam DT.

scholarly journals Аlgorithmization of the knowledge base in service of passenger cars

2022 ◽  
Vol 14 (2) ◽  
pp. 80-88
Author(s):  
Viacheslav Pavlenko ◽  
◽  
Volodymyr Manuylov ◽  
Volodymyr Kuzhel ◽  
Vladislav Listgarten ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Intelligent Systems ◽  
Driving Forces ◽  
Conceptual Modeling ◽  
Subject Area ◽  
Detailed Knowledge ◽  
Passenger Cars ◽  
Subject Areas ◽  
The Subject

The article considers the architecture of conceptual modeling of the knowledge base, which creates a model of the subject area in the form of many concepts and relationships between them. This approach is based on the concept of a mobile software agent, which is implemented and functions as an independent specialized computer program or an element of artificial intelligence. Ensuring the use of subject area knowledge has become one of the driving forces of the recent surge in the study of artificial intelligence. For example, for models of many different subject areas it is necessary to formulate the concept of time. This representation includes the concepts of time intervals, time points, relative measures of time, etc. If one group of scientists develops a detailed knowledge base, others can simply reuse it in their subject areas using their own database. Creating explicit assumptions in the subject area, which underlie the implementation, makes it easy to change the assumptions when changing our knowledge of the subject area. The process of conceptualization of TO and P, first of all, involves the development of databases in research areas for the formalization and systematization of knowledge about the characteristics of this area of entities and phenomena. That is, the use of concepts in the field of maintenance in a consistent manner in relation to theories of knowledge. Ultimately, the paper updated mathematical modeling, algorithmization and implementation of intelligent systems in the field of maintenance, which will help automate the process of diagnosis and inspection of all car systems, facilitate fault prevention and improve the maintenance process and modernize the maintenance system itself. The approach of algorithmization of the base of knowledge of a condition of the car in each moment of time considered in work gives the chance to reduce time of stay of the car in the service center and to reduce considerably expenses for passing of MOT at service of cars.

scholarly journals Ontological approach to automatic generation of questions in intelligent learning systems

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (5) ◽  
pp. 44-52
Author(s):  
Li Wenzu
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Structural Characteristics ◽  
Automatic Generation ◽  
Knowledge Bases ◽  
Subject Area ◽  
Learning Systems ◽  
Multiple Choice Questions ◽  
Technical Standards ◽  
Ontological Approach

This article proposes an approach for designing a general subsystem of automatic generation of questions in intelligent learning systems. The designed subsystem allows various types of questions to be automatically generated based on information from the knowledge bases and save the generated questions in the subsystem knowledge base for future use. The main part of the subsystem is the automatic generation module of questions, which allows one to generate questions of various types based on existing question generation strategies in combination with the structural characteristics of knowledge bases built using OSTIS technology. In this article, a variety of strategies for automatically generated questions are proposed, the use of which allows various types of questions to be automatically generated, such as multiple-choice questions, fill-in-the-blank questions, questions of definition interpretation and etc. The most important part of the subsystem is the knowledge base, which stores the ontology of questions, including the question instances themselves. In this article, the knowledge base is constructed based on OSTIS technical standards. The type classification of automatically generated questions was developed, as well as the subject area for storing generated questions and the corresponding ontology described in the knowledge base of the subsystem. The generated questions are stored in the subsystem knowledge base in the form of SC-code, which is the OSTIS technology standard. When testing users, these automatically generated questions are converted to the corresponding natural language form through the natural language interface. Compared with the existing approaches, the approach proposed in this article has certain advantages, and the subsystem designed using this approach can be used in various OSTISbased systems driven by OSTIS technology.

Typology of regions by functional role in Russian innovation space

2019 ◽  
pp. 46-57
Author(s):  
A.S. Mikhaylov ◽  
T. Yu. Kuznetsova ◽  
I. Yu. Peker
Keyword(s):  
Innovation Process ◽  
Subject Area ◽  
Knowledge Generation ◽  
Specific Knowledge ◽  
Factors Affecting ◽  
Knowledge Domains ◽  
New Knowledge ◽  
The Subject ◽  
The Russian Federation ◽  
Innovation Space

The article is devoted to studying the spatial heterogeneity of innovation space across Russia while assessing the regional divergence and concentration of knowledge-generating centers. The preliminary hypothesis suggests there are three types of regions: growth poles - the largest cities and agglomerations that generate a significant amount of new knowledge of wide specialization spectrum; zones of influence - territories with high generative potential in one or several specific knowledge domains; and innovation peripheries that demonstrate weak ability to generate new knowledge. The analysis of an array of indicators on publication activity over 2013-2017 across the regions of the Russian Federation using the method of spatial scientometrics has clarified and detailed the initial typology of regions according to their generative function in the interregional innovation process. The study showed the importance of a complex of metropolitan, coastal and border factors affecting the innovative potential of territories. For coastal regions, incl. having a cross-border position, a higher level of research productivity and integration into international S&T cooperation is characteristic, as well as a general positive dynamics of new knowledge generation if found. In addition, the coastal factor determines the specificity of the subject area of the intellectual capital created and the knowledge base accumulated in the region.

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