A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Abstract To obtain innovative concepts in the design, designers often need to retrieve and use interdisciplinary knowledge. Concept–knowledge (C–K) theory emphasizes the role of knowledge and introduces the knowledge (K) space and concept (C) space, employing operators to transform the contents between these spaces. Some studies, based on this theory, have successfully provided designers with different forms of knowledge to stimulate concept generation. However, the amount of knowledge provided in these studies is limited, and they fail to offer convenient methods for knowledge retrieval and reasoning, making it challenging to meet the needs of conceptual design across different fields. This paper proposes an enhanced C–K method leveraging large language models (LLMs) to help designers retrieve knowledge and uncover potentially new concepts. Our method redefines the C space and K space within the context of LLMs, dividing the properties of concept into function, appearance, and technology, and requiring the knowledge to correspond to these properties, thereby facilitating a structured connection between concepts and knowledge. Based on this definition, we achieved flexible knowledge retrieval and concept ideation leveraging LLMs. We also conducted a case study on wearable devices to validate our method. The results showed that our method helped designers to retrieve professional knowledge and inspired them to create feasible and innovative concepts.

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Publication Details

Journal
Journal of Computing and Information Science in Engineering
Published
2024-10-03
DOI
https://doi.org/10.1115/1.4066773
Citations
15
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
11.69

Funders

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article

A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Liuqing Chen, Lingyun Sun, Duowei Xia, Lin Zhang et al.
15 citations
Journal of Computing and Information Science in Engineering
Technology and Data Analysis
11.69
article

A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Liuqing Chen, Lingyun Sun, Duowei Xia, Lin Zhang, Xinyang Tan, Zhaojun Jiang
article en
15 citations

Abstract

Abstract To obtain innovative concepts in the design, designers often need to retrieve and use interdisciplinary knowledge. Concept–knowledge (C–K) theory emphasizes the role of knowledge and introduces the knowledge (K) space and concept (C) space, employing operators to transform the contents between these spaces. Some studies, based on this theory, have successfully provided designers with different forms of knowledge to stimulate concept generation. However, the amount of knowledge provided in these studies is limited, and they fail to offer convenient methods for knowledge retrieval and reasoning, making it challenging to meet the needs of conceptual design across different fields. This paper proposes an enhanced C–K method leveraging large language models (LLMs) to help designers retrieve knowledge and uncover potentially new concepts. Our method redefines the C space and K space within the context of LLMs, dividing the properties of concept into function, appearance, and technology, and requiring the knowledge to correspond to these properties, thereby facilitating a structured connection between concepts and knowledge. Based on this definition, we achieved flexible knowledge retrieval and concept ideation leveraging LLMs. We also conducted a case study on wearable devices to validate our method. The results showed that our method helped designers to retrieve professional knowledge and inspired them to create feasible and innovative concepts.

Journal of Computing and Information Science in EngineeringVol. 25(2)
Zhejiang University of Science and Technology (CN), Shanghai Jiao Tong University (CN), Zhejiang Hospital (CN), Zhejiang University of Technology (CN), Zhejiang University (CN)
Chinese Medicine Research Program of Zhejiang Province
Openalex Percentile: Top 2%
Technology and Data Analysis
11.69
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