A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Zhang, Yufei; Wang, Hongwei; Zhai, Xiang; Zhao, Yanwei; Guo, Jing
• Is Part Of: Systems science & control engineering. Volume 9:Issue 1(2021)
• Page Start: 497
• Page End: 513
• Publication Date: 2021-01-01
• Subjects: Product design -- knowledge representation model -- cognitive process -- knowledge graph -- knowledge reuse; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2021.1937373
• Languages: English
• ISSNs: 2164-2583

Abstract

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.