Evaluation of the cognitive ergonomics of ontologies on the basis of graph analysis

Gavrilova, Tatiana; Gorovoy, Vladimir; Bolotnikova, Ekaterina

doi:10.3103/s0147688210060043

Cited by 7 publications

(5 citation statements)

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“…While knowledge sharing speed, that is, the speed of the requested knowledge transfer from the knowledge holder to the requester, is a considerably clear and homogeneous category, knowledge sharing quality is much more complicated and can be divided into several subcategories. A considerable amount of structures of such subcategories has been suggested in the literature (Cabrera & Cabrera, 2002 ; Rumanti et al, 2018 ; Witherspoon et al, 2013 ); one of the possible high‐level classifications of knowledge quality criteria divides these into, for example, knowledge sharing fullness (i.e., does the knowledge holder transfer all the volume of the requested knowledge item) and adequacy (i.e., does the knowledge holder react to the knowledge sharing request by transferring exactly that knowledge item that is being requested) (Blagov et al, 2020 ; Doronin et al, 2020 ; Gavrilova et al, 2012 ; Younis & Adel, 2020 ).…”

Section: Research Backgroundmentioning

confidence: 99%

The onlinezation influence on knowledge sharing for corporate innovation during theCoViDcrisis

Blagov

Anand

2022

Knowl Process Manag

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The article describes the study of the influence of shock onlinezation caused by the CoViD‐19 quarantine on the knowledge management performance and effectiveness in organizations. The methodology of the study is based on a set of multiple linear regression equations linking together the onlinezation, knowledge management parameters, problem‐solving and financial performance. The results of the study allow developing a set of practical recommendations regarding the development of knowledge management systems in organizations under the onlinezation context, with a special accent to solving technical and/or marketing uncertainty‐related problems with architectural or modular innovations.

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Section: Research Backgroundmentioning

confidence: 99%

The onlinezation influence on knowledge sharing for corporate innovation during theCoViDcrisis

Blagov

Anand

2022

Knowl Process Manag

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“…This circumstance, first of all, is caused by the impossibility of their practical application to solve the identified issues due to the existing features of the topological organization of the formed decomposition structures of technologies (DST) [6]. In this regard, we propose our own set of indicators of formalized description quality and analysis of technologies, obtained on the basis of studying the topology of the obtained ontological representations [7], which is quite consistent with the general approaches implemented in this field [8] - [10].…”

Section: Introductionmentioning

confidence: 88%

Indicators of Formalized Description Quality and Analysis of Production Technologies

Andreev

Dementiev

Lyokhin

et al. 2021

ETR

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This paper discusses conceptual basis for assessment and analysis of model quality relating to formalized description of technologies. The authors give indicators used to assess formalized and textual descriptions of technologies. They raise some questions concerning the analysis of technologies that enable to make a list of indicators. The paper examines decomposition structures of specific technologies and provides the calculations of introduced coefficients.

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“…Evaluating of the proposed ontology, the Ontology cognitive ergonomics evaluation based on graph topology (Gavrilova et al, 2010a(Gavrilova et al, , 2010bOntoeval) is implemented to analyze the structure of the ontology. The aim of this evaluation method is to assess ontology balance and its perception by humans.…”

Section: Ontology Evaluationmentioning

confidence: 99%

“…Yngve-Miller shows percentage of concepts node and number of links which is hard or easy to perceive by humans. The greater percentage value, the ontology is easy to perceive by humans (Gavrilova et al, 2010a(Gavrilova et al, , 2010b.…”

Section: Ontology Evaluationmentioning

confidence: 99%

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An Application of Structural Equation Modeling for Developing Good Teaching Characteristics Ontology

Phiakoksong¹,

Niwattanakul²,

Angskun

2013

Informatics in Education

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Ontology is a knowledge representation technique which aims to make knowledge explicit by defining the core concepts and their relationships. The Structural Equation Modeling (SEM) is a statistical technique which aims to explore the core factors from empirical data and estimates the relationship between these factors. This article presents an application of Structural Equation Modeling to construct a lightweight ontology of good teaching characteristics. The principal factors from SEM are used as concepts of the proposed ontology. The OAsys approach is used as an ontology development process. The proposed ontology consists of three levels: super-class level consists of 6 components of teaching; the first-level sub-class consists of 12 latent factors from SEM; and the second-level sub-class consists of 66 classes of questionnaire items. The experimental result indicated that the proposed ontology which constructs from the core factors of SEM is well-structured, free from a node with multiple parent classes. There is no cycles loop between sub-classes. Having well-balanced of ratio between parent classes and sub-classes. The structure of this proposed ontology corresponds to the principle of conceptual balance.

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Evaluation of the cognitive ergonomics of ontologies on the basis of graph analysis

Cited by 7 publications

References 4 publications

The onlinezation influence on knowledge sharing for corporate innovation during theCoViDcrisis

The onlinezation influence on knowledge sharing for corporate innovation during theCoViDcrisis

Indicators of Formalized Description Quality and Analysis of Production Technologies

An Application of Structural Equation Modeling for Developing Good Teaching Characteristics Ontology

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