TOWARDS IMPLEMENTATION OF COMPONENT-BASED MODELING IN THE PLATFORM FOR CREATING KNOWLEDGE-BASED SYSTEMS

В статье исследуются проблемы автоматизации и интеллектуальной поддержки процесса математического и имитационного моделирования сложных объектов за счёт комбинации компонентно-ориентированного и онтологического подходов. В качестве основной прикладной области для применения обсуждаемых методов и средств предполагается использовать такое направление, как комплексное моделирование окружающей среды. В контексте изучаемых вопросов рассмотрены современные подходы к автоматизации компонентно-ориентированного моделирования. При интеграции компонентов-моделей в единую результирующую комплексную модель разработчику необходимо не только обеспечить формальное согласование со стандартами используемого каркаса моделирования, но и учитывать различные типы семантической и синтаксической неоднородности компонентов. В связи с этим выполнена классификация типов интеграции комплексных моделей, обсуждаются особенности реализации компонентно-ориентированного моделирования в авторской платформе создания систем, основанных на знаниях. В качестве иллюстративного примера рассматривается гидролого-экологическая балансовая модель. The article considers the problems of automation and intellectual support of the mathematical and simulation modeling process of complex objects via a combination of component-based and ontological approaches. As the main application area for the discussed methods and tools, it is proposed to use the integrated environmental modeling domain. In this context, modern approaches to the automation of component-based modeling are considered. To couple model components into a final complex model, the developer needs not only to ensure formal agreement with the standards of the modeling framework but also to take into account various types of semantic and syntactic heterogeneity of components. In this regard, the classification of the integration types for complex modeling is carried out, the related implementation features in the author's platform for creating knowledge-based systems are discussed. The hydrological-ecological balance model is considered an illustrative example.

Modeling Frameworks for Knowledge Engineering Approaches

Human knowledge was regarded as a transfer process into an applied knowledge base in the early 1980s as the creation of a Knowledge-Based Systems (KBS). The premise behind this transfer was that the KBS-required information already existed and only needed to be gathered and applied. Most of the time, the necessary information was gleaned through talking to professionals about how they handle particular problems. This knowledge was usually put to use in production rules, which were then carried out by a rule interpreter linked to them. Here, we demonstrate a number of new ideas and approaches that have emerged during the last few years. This paper presents MIKE, PROTÉGÉ-II, and Common KADS as three different modeling frameworks that may be used together or separately.

Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.

A Combining Method for Systems Requirement of Knowledge - Based Medical Hazardous Waste

Applying Knowledge-Based Systems (KBS) for hazardous medical waste (HMW) are still limited according to knowledge sources. The two mechanisms of the KBS are commonly known as rulebased and case-based, which can be extracted based on existing regulations that are divided into global, national, provincial, and local authorities. This study used Soft System Methodology and regulation approaches to find out the gaps in coordinating and managing HMW in Indonesia. It is used to depict the rule, processes, and stakeholders for identifying the features of the KBS. The study was taken using fourstage of SSM conducted to classify the objects and their feature requirements. The knowledge source was verified and validated using Focus Group Discussion and represented on CATWOE mode. Most of the regulations are not complying with the practicality of the HMW since it is produced until disposal. The methods give an alternative for knowledge engineers to feed the basis of knowledge.

Knowledge-Based Systems Manufacturing With Viability Ensuring

With highly increased competition, intelligent product manufacturing based on interpretable knowledge bases has been recognized as an effective method for building applications of explainable Artificial Intelligence that is the hottest topic in the field of Artificial Intelligence. The success of product family directly depends on how effective the viability mechanisms are laid down in its design. In this paper, a systematic cloud-based set of tool family is proposed to develop viable knowledge-based systems. For productive participation of domain and cognitive specialists in manufacturing, the knowledge base should be declarative, testable and integratable with other architectural components. Mechanisms to ensure KBS viability are provided in an ontology-oriented development environment, where each component is formed in terms of domain ontology by using the adaptable instrumental support. Due to the explicit separation of ontology from knowledge, it became possible to divide competencies between specialists creating an ontology and specialists creating a knowledge base. We rely on the fact that the activity of creating an ontology is significantly different from the activity of creating a knowledge base. Creating an ontology is a creative process that requires a systematic analysis of the domain area in order to identify common patterns among its knowledge.The characteristic properties of knowledge-based systems related to viability are described. It is explained, how these properties are provided in development environments implemented on cloud platform. The concept of a specialized manufacturing environment for knowledge-based system is introduced. The necessary set of tools for such ontology-oriented environment construction is determined. The example of tools for creating specialized manufacturing environments is the instruments implemented on the «IACPaaS» platform. The IACPaaS is already used for collective development of thematic cloud knowledge portals with viable knowledge-based systems. This specialized manufacturing environment has enabled the creation of multi-purpose medical software services to support specialist solutions based on knowledge being remotely improved by experts.

Potential for combining semantics and data analysis in the context of digital twins

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum

Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain.

Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.