  Design representation is a common task in the design process to facilitate
learning, analysis, redesign, communication, and other design activities.
Traditional representation techniques rely on human expertise and manual
construction and are difficult to repeat and scale. Here, we propose a
methodology that utilizes a pre-trained large-scale cross-domain design
knowledge base to automatically generate design representation as a semantic
network, i.e., a network of the entities and relations, based on design
descriptions in texts or natural languages. Our methodology requires no ad hoc
statistics. Based on a participatory study, we reveal the effectiveness and
differences of the semantic network representations that are automatically
generated with alternative knowledge bases. The findings illuminate future
research directions to enhance design representation as semantic networks.
