Empirical Basis of Engineering Design Knowledge

Engineering design knowledge is embodied in natural language text through intricate placement of entities and relationships. Ontological constructs of design knowledge often limit the performances of NLP techniques to extract design knowledge. Also, large-language models could be less useful for generating and explicating design knowledge, as these are trained predominantly on common-sense text. In this article, we present the constituents of design knowledge based on empirical observations from patent documents. We obtain a sample of 33,881 patents and populate over 24 million facts from the sentences in these. We conduct Zipf distribution analyses using the frequencies of unique entities and relationships that are present in the facts thus populated. While the literal entities cannot be generalised from the sample of patents, the relationships largely capture attributes ('of'), structure ('in', 'with'), purpose ('to', 'for'), hierarchy ('include'), exemplification ('such as'), and behaviour ('to', 'from'). The analyses reveal that over half of entities and relationships could be generalised to 64 and 24 linguistic syntaxes respectively, while hierarchical relationships include 75 syntaxes. These syntaxes represent the linguistic basis of engineering design knowledge. We combine facts within each patent into a knowledge graph, from which we discover motifs that are statistically over-represented subgraph patterns. Across all patents in the sample, we identify eight patterns that could be simplified into sequence [->...->], aggregation [->...<-], and hierarchy [<-...->] that form the structural basis of engineering design knowledge. We propose regulatory precepts for concretising abstract entities and relationships within subgraphs, while also explicating hierarchical structures. These precepts could be useful for better construction and management of knowledge in a design environment.