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A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

Authors :
Yufei Zhang
Hongwei Wang
Xiang Zhai
Yanwei Zhao
Jing Guo
Source :
Systems Science & Control Engineering, Vol 9, Iss 1, Pp 497-513 (2021)
Publication Year :
2021
Publisher :
Taylor & Francis Group, 2021.

Abstract

Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph.

Details

Language :
English
ISSN :
21642583
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Systems Science & Control Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.0319a821c3af482fbde67c27b6811a4a
Document Type :
article
Full Text :
https://doi.org/10.1080/21642583.2021.1937373