Intelligent Technology for Integrated Expert Systems Construction

Rybina, Galina V.; Rybin, V. M.; Blokhin, Yury M.; Sergienko, Elena S.

doi:10.1007/978-3-319-33816-3_19

Cited by 11 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This activity should be carried out by cognitive scientists (knowledge engineers) together with experts. When creating an ontology, it is important to create it in such a way that it "covers" a class of tasks (for example, an ontology of knowledge on the diagnosis of diseases regardless of their etiology [42], an ontology of knowledge on the modes of laser additive manufacturing [43]. The ontology is generally not changeable throughout the KBS lifecycle.…”

Section: Discussionmentioning

confidence: 99%

“…Such problems in modern KBSs are solved "using heuristics, including empirical induction, analogy and deduction" (Finn 2014), often using more than one method of imitating human intellectual activity (Gavrilova et al 2020). Modern KBSs offer a convenient user interface, they can receive data from measurement instrumentation and are often integrated with statistical and other software components that do not use formalized knowledge (including standard software packages) (Rybina et al 2011;Rybina 2008).…”

Section: The Features Of Knowledge-based Software Systemsmentioning

confidence: 99%

“…The methods and technologies proposed in (Fikes and McGuinness 2000;Musen 2015; Mortensen et al 2015; Rybina et al 2011;Rybina 2008) make it possible to create knowledge bases and knowledge-based systems, test them and put them into operation. However, not enough attention is paid to the methods and tools for improvement of their components, especially in process of their operation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge-Based Systems Manufacturing With Viability Ensuring

Грибова

Шалфеева

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Features Of Knowledge-based Software Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge-Based Systems Manufacturing With Viability Ensuring

Грибова

Шалфеева

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Модель прикладной онтологии курса/дисциплины (Ме/ Мо), представляющая собой семантическую сеть, описывается в виде «…M e = <V е , U е , С, K e , RK, Ie, Se>, где V е -множество элементов курса/ дисциплины (разделов, тем, подтем и т.д. ), которые представляются как Детальное описание основных концепций интеллектуальной технологии построения ИЭС, формальное описание модели интеллектуальной программной среды и опыта реализации её отдельных компонентов приводится в монографиях автора, например [1,2], а также в ряде работ [6,19,20] и др., поэтому рассмотрим кратко только те основные определения и понятия, которые наиболее важны для прототипирования обучающих ИЭС.…”

Section: базовая модель онтологии курса/дисциплиныunclassified