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Idea generation with Technology Semantic Network
Serhad Sarica, Binyang Song, Jianxi Luo, Kristin L. Wood
Journal:

AI EDAM / Volume 35 / Issue 3 / August 2021

Published online by Cambridge University Press:

03 March 2021, pp. 265-283
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There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.