Social-Knowledge Big Graph Integrating Ontology and Social Network on the Perspective of “Knowledge Association”: A Case Study of Knowledge Organization in Financial Market

The rapid growth of digital social information service platforms has fundamentally transformed information diffusion in financial markets, creating an urgent need for knowledge organization systems that can integrate structured financial knowledge with dynamic social interactions. Traditional approaches, which rely on either ontological precision or social network analysis alone, face significant limitations in capturing the complex, multimodal associations that drive modern market behavior. This study proposes a novel Social-Knowledge Big Graph (SKBG) framework to address this gap. The SKBG is designed from a knowledge association perspective and systematically models five core patterns through a three-layer fusion model. The framework semantically grounds social entities in ontologies, enables consistent reasoning and knowledge discovery, and captures temporal dynamics and influence propagation. A case study of China’s A-share market and associated investor communities demonstrates the SKBG’s practical value. It identifies latent “cross-modal influencer” archetypes, traces semantically enriched misinformation propagation paths that reveal coordination mechanisms, and uncovers socially mediated stock price comovement that traditional models miss. These findings validate the SKBG as a knowledge organization system that bridges the divide between discrete social interactions and complex financial dynamics. This study contributes a robust methodological framework for constructing large-scale socio-financial knowledge organization systems, with important implications for intelligent financial services, risk surveillance, and market regulation in the digital age.