  There have been growing uses of semantic networks in the past decade, such as
leveraging large-scale pre-trained graph knowledge databases for various
natural language processing (NLP) tasks in engineering design research.
Therefore, the paper provides a survey of the research that has employed
semantic networks in the engineering design research community. The survey
reveals that engineering design researchers have primarily relied on WordNet,
ConceptNet, and other common-sense semantic network databases trained on
non-engineering data sources to develop methods or tools for engineering
design. Meanwhile, there are emerging efforts to mine large scale technical
publication and patent databases to construct engineering-contextualized
semantic network databases, e.g., B-Link and TechNet, to support NLP in
engineering design. On this basis, we recommend future research directions for
the construction and applications of engineering-related semantic networks in
engineering design research and practice.
