D-Graph: AI-Assisted Design Concept Exploration Graph
release_wdpv2tijfffb7eatisqhoizqpa
by
Shin Sano, Seiji Yamada
2022
Abstract
We present an AI-assisted search tool, the "Design Concept Exploration Graph"
("D-Graph"). It assists automotive designers in creating an original
design-concept phrase, that is, a combination of two adjectives that conveys
product aesthetics. D-Graph retrieves adjectives from a ConceptNet knowledge
graph as nodes and visualizes them in a dynamically scalable 3D graph as users
explore words. The retrieval algorithm helps in finding unique words by ruling
out overused words on the basis of word frequency from a large text corpus and
words that are too similar between the two in a combination using the cosine
similarity from ConceptNet Numberbatch word embeddings. Our experiment with
participants in the automotive design field that used both the proposed D-Graph
and a baseline tool for design-concept-phrase creation tasks suggested a
positive difference in participants' self-evaluation on the phrases they
created, though not significant. Experts' evaluations on the phrases did not
show significant differences. Negative correlations between the cosine
similarity of the two words in a design-concept phrase and the experts'
evaluation were significant. Our qualitative analysis suggested the directions
for further development of the tool that should help users in adhering to the
strategy of creating compound phrases supported by computational linguistic
principles.
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