Embedding Experiential Design Knowledge in Interactive Knowledge Graphs

Wang Y, Goridkov N, Rao V, Cui D, Grandi D, and Goucher-Lambert K. 2023. Journal of Mechanical Design (2023).


Knowledge collection, extraction, and organization are critical activities in all aspects of the engineering design process. However, it remains challenging to surface and organize design knowledge, which often contains implicit or tacit dimensions that are difficult to capture in a scalable and accessible manner. Knowledge graphs (KGs) have been explored to address this issue, but have been primarily semantic in nature in engineering design contexts, typically focusing on sharing explicit knowledge. Our work seeks to understand knowledge organization during an experiential activity and how it can be transformed into a scalable representation. To explore this, we examine 23 professional designers’ knowledge organization practices as they virtually engage with data collected during a teardown of a consumer product. Using this data, we develop a searchable knowledge graph as a mechanism for representing the experiential knowledge and afford its use in complex queries. We demonstrate the knowledge graph with two extended examples to reveal insights and patterns from design knowledge. These findings provide insight into professional designers’ knowledge organization practices and represent a preliminary step toward design knowledge bases that more accurately reflect designer behavior, ultimately enabling more effective data-driven support tools for design.