A bottom-up approach of knowledge graph modelling for urban underground public spaces: Insights into public cognition

Urban underground public spaces are increasingly recognised as crucial for enhancing urban functionality and liveability. However, effectively integrating public cognition into underground public space planning remains challenging. This study addresses this gap by proposing a novel approach integrating natural language processing, knowledge graphs and complex network theory to systematically mine public cognition from WeChat official account articles and Web of Science abstracts. Integrating the principles of linguistics and semiotics, we developed a novel approach for knowledge elements extraction and vectorised knowledge graph construction. This yielded 15,335 Chinese and 10,589 English effective knowledge elements, which were used to construct and compare knowledge graphs representing the cognitive structures of social and academic communities. Findings reveal distinct priorities, with the social community emphasising experiential aspects while the academic community focuses on theoretical concepts. Network analysis underscores the scale-free nature of both graphs, with higher centrality in the social network. These insights offer valuable implications for developing tailored public participation strategies in underground space planning, promoting more inclusive and human-centred urban development.