Design and innovation processes primarily generate knowledge upon retrieving and synthesising knowledge of existing artefacts. Understanding the basis of knowledge governing these processes is essential for theoretical and practical advances, especially with the growing inclusion of Large-Language Models (LLMs) and their generative capabilities to support knowledge-intensive tasks. In this research, we analyse a large, stratified sample of patented artefact descriptions spanning the total technology space. Upon representing these descriptions as knowledge graphs, i.e., collections of entities and relationships, we investigate the linguistic and structural foundations through frequency distribution and motif discovery approaches. From the linguistic perspective, we identify the generalisable syntaxes that show how most entities and relationships are constructed at the term level. From the structural perspective, we discover motifs, i.e., statistically dominant 3-node and 4-node subgraph patterns, that show how entities and relationships are combined at a local level in artefact descriptions. Upon examining the subgraphs within these motifs, we understand that artefact descriptions primarily capture the design hierarchy of artefacts. We also find that natural language descriptions do not capture sufficiently precise knowledge at a local level, which can be a limiting factor for relevant innovation research and practice. Moreover, our findings are expected to guide LLMs in generating knowledge pertinent to domain-specific design environments, to inform structuring schemes for future knowledge management systems, and to advance design and innovation theories on knowledge synthesis.
Siddharth et al. (2025) studied this question.