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A multi-case induction adaptation study of tacit knowledge based on NRS and CBR

A multi-case induction adaptation study of tacit knowledge based on NRS and CBR This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.Design/methodology/approachFirstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.FindingsThe findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.Research limitations/implicationsIn this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.Practical implicationsThe examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.Social implicationsThe algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.Originality/valueThis study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

A multi-case induction adaptation study of tacit knowledge based on NRS and CBR

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References (43)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0368-492X
DOI
10.1108/k-01-2023-0049
Publisher site
See Article on Publisher Site

Abstract

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.Design/methodology/approachFirstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.FindingsThe findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.Research limitations/implicationsIn this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.Practical implicationsThe examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.Social implicationsThe algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.Originality/valueThis study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

Journal

Kybernetes – Emerald Publishing

Published: Oct 30, 2024

Keywords: Tacit knowledge; Case adaptation; Neighbourhood rough sets; Parsimony; Game theory

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