Skip to main navigation Skip to search Skip to main content

Idea generation with Technology Semantic Network

Serhad Sarica*, Binyang Song, Jianxi Luo, Kristin L. Wood

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

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. © The Author(s), 2021. Published by Cambridge University Press.
Original languageEnglish
Pages (from-to)265-283
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume35
Issue number3
Online published3 Mar 2021
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Research Keywords

  • Data-driven design
  • flying car
  • idea generation
  • semantic distance
  • Technology Semantic Network

Fingerprint

Dive into the research topics of 'Idea generation with Technology Semantic Network'. Together they form a unique fingerprint.

Cite this