Knowledge Graph-Driven Semantic Association Analysis of Traditional Cultural Symbols and Intelligent Design of Cultural and Creative Products

Abstract

In light of the rapid progress made in the digital transformation of cultural resources and intelligence in cultural creativity industries, there are issues with traditional cultural creativity design, including insufficient research into cultural semantics, low precision in matching elements in the design process, and poor effectiveness in solution discovery. To solve this issue, the use of knowledge graphs and semantic computing techniques to model cultural semantics and semantic relationships is proposed to develop an intelligent design system based on semantic association modeling. This paper proposes the structural representation of cultural symbols and models various types of semantic relationships and uses the graph embedding technique to enable vector representation of symbolic semantics and path reasoning to explore implicit semantic relationships. On this basis, the “cultural semantics-design element” mapping strategy is formed by mapping the semantics to color, pattern, and materials of the design, as well as multielement optimization under semantic constraints.Moreover, a mechanism for human-computer collaboration optimization and dynamic graph updating is established in order to ensure continuous iteration and precise control over the design solution. It can be seen that the above-mentioned method performs outstandingly well when it comes to cultural creativity design-related work: semantic matching precision is raised from 0.72 to 0.89; design consistency is improved from 0.75 to 0.86; while user acceptability is increased from 0.70 to 0.84. These metrics have been further improved during the process of optimization through iterations to 0.91, 0.89, and 0.88 respectively, illustrating that the method has successfully helped enhance the semantic precision of culture expressions and consistency in the whole design results.