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 Intelligent decision support method in a typical situation

2021 ◽  
Vol 11 (1) ◽  
pp. 126-136
Author(s):  
V.V. Antonov ◽  
◽  
K.A. Konev
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Business Process ◽  
Support System ◽  
System Integration ◽  
General Structure ◽  
Typical Situation ◽  
Ontological Model ◽  
Decision Support Method

The article discusses a decision support method using a knowledge base. The relevance of the study of issues related to decision-making in typical situations is shown. In order to increase the effectiveness of management activities, the is-sues of system integration of the regulatory framework, ontological model and knowledge base of the intelligent sub-system of decision support within the framework of the business process are considered. In support of the proposed method, a model has been formed for replenishing the knowledge base both at the structural and analytical levels, which demonstrates the connection between the most important elements of the system: the ontological model, the knowledge base and the normative subsystem. An example of using the proposed scheme is shown. To demonstrate the model of functioning of the decision support system, an algorithm for replenishing the knowledge base is proposed and described. As a conceptual basis for the formal description of the model, operations for working with knowledge are described in the set-theoretic aspect. The principles of adaptation of the ontological model as an information object for linking with the knowledge base are considered. The conceptual diagram of the general structure of the ontological model for making decisions within the framework of the business process as a set of interrelated concepts is proposed and demonstrated. An information model of a specialized database has been developed and presented, serving as a technical basis for building a knowledge base of a decision support system in a typical situation, its main structural el-ements, the principles of their interrelation and an approach to ensuring the consistency of its internal structure are de-scribed.

scholarly journals DEFINITION OF THE CATEGORY OF ROOMS FOR EXPLOSION AND FIRE HAZARDS USING ONTOLOGICAL SIMULATION

2021 ◽  
Vol 56 ◽  
pp. 112-118
Author(s):  
Tatiana E. Shulga ◽  
◽  
Yuliya V. Nikulina ◽  
Keyword(s):  
Decision Support ◽  
Support System ◽  
Fire Hazard ◽  
Subject Area ◽  
Systematic Analysis ◽  
Fire Hazards ◽  
Regulatory Documents ◽  
The Subject ◽  
Ontological Model ◽  
Definition Of

The article formalizes the task of determining the category of premises for explosion and fire hazard in accordance with regulatory documents. A systematic analysis of the process of its solution is carried out and a model of the subject area in the form of an ontology is proposed. Based on the analysis of the regulatory documentation, the scheme for determining the category of the premises was formalized in the form of an algorithm. The authors have identified the main criteria for determining the belonging of a room to a certain category. Examples of using the algorithm for determining the category of a room on real practical problems are considered. The ontological model obtained as a result of the analysis is used as the basis for the decision support system.

FORMALIZATION OF THE PROBLEM OF MATHEMATICAL INTERPRETATION RECOGNITION OF MEDICAL IMAGES FOR EXAMPLE ALOPECIA

2021 ◽  
pp. 126-129
Author(s):  
Ахмед Магомедович Денгаев
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Image Recognition ◽  
Support System ◽  
Medical Knowledge ◽  
Recognition System ◽  
Subject Area ◽  
Main Task ◽  
Medical Image Recognition ◽  
Mathematical Interpretation

Одним из перспективных и эффективных направлений автоматизированной диагностики заболевания является использование системы распознавания медицинских образов. Главная задача - это максимально точная интерпретация изображения. Прежде всего, необходимо правильно формализовать задачу, провести структуризацию основных условий функционирования системы. В статье составлено содержательное описание предметной области, разработано формализованная схема исследуемой системы. Обозначено, что решение задачи структуризации сводится к разработке отдельных классов, сгруппированных по общим признакам и характеристикам болезни. В этом случае точность и информативность диагноза будет зависеть от полноты базы данных конкретного класса. Приведена математическая интерпретация отношений и связей элементов системы. Применение математических моделей и алгоритмов в медицине является важной задачей. Выбор того или иного алгоритма определяется решаемой задачей. Необходимо понимать, что получаемый результат особо ценен, если он подтверждается математическими расчетами. Предложена многоуровневая архитектура системы поддержки принятия решений, где ключевое место отведено модулю автоматизированной диагностики и распознавания изображения. Отмечено, что при создании системы поддержки принятия решения в медицине специалисты сталкиваются с двумя концептуальными барьерами: первый - связан с колоссальным объемом медицинских знаний, а второй - с постоянным обновлением этих знаний и технологий их обработки. Поэтому главной задачей является правильная структуризация и формализация системы поддержки принятия решений для его эффективного применения One of the most promising and effective areas of automated diagnosis of the disease is the use of a medical image recognition system. The main task is to interpret the image as accurately as possible. First of all, it is necessary to properly formalize the task, to structure the basic conditions for the functioning of the system. The article contains a meaningful description of the subject area, and a formalized scheme of the system under study is developed. It is indicated that the solution to the problem of structuring is reduced to the development of separate classes grouped by common signs and characteristics of the disease. In this case, the accuracy and informativeness of the diagnosis will depend on the completeness of the database of a particular class. The mathematical interpretation of the relations and connections of the system elements is given. The application of mathematical models and algorithms in medicine is an important task. The choice of an algorithm is determined by the problem being solved. It is necessary to understand that the result obtained is particularly valuable if it is confirmed by mathematical calculations. A multi-level architecture of the decision support system is proposed, where the key place is given to the module of automated diagnostics and image recognition. It is noted that when creating a decision support system in medicine, specialists face two conceptual barriers: the first one is associated with a huge amount of medical knowledge, and the second one is associated with the constant updating of this knowledge and technologies for their processing. Therefore, the main task is to properly structure and formalize the decision support system for its effective application

scholarly journals The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yicheng Jiang ◽  
Bensheng Qiu ◽  
Chunsheng Xu ◽  
Chuanfu Li
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Clinical Decision Support ◽  
Support System ◽  
Naive Bayes ◽  
Clinical Decision ◽  
Disease Symptom ◽  
Layer Model ◽  
Bayes Algorithm

In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.

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