社交知识大图的计算推理方法与应用展望

Social-knowledge big graphs inherit and enrich the concepts of social networks and knowledge graphs, covering a variety of human-centered entities and dynamically changing multiple semantic associations. It is used in application scenarios, such as knowledge discovery, reasoning, and social recommendations in many fields. These materials have broad application prospects and value. This article focuses on exploring the value of social-knowledge graphs in computational reasoning and application scenarios, and looks forward to future development trends. Compared with traditional knowledge representation learning and knowledge reasoning, social-knowledge graphs are used in computing and reasoning tasks for complex decision-making scenarios because of their multifaceted nature, ease of calculation, dynamic timeliness, and interactive contagion. It has significant advantages including higher algorithm integration, innovation, and interpretability. In specific application scenarios, social-knowledge graphs have shown good application prospects in fields such as smart finance, social e-commerce, intelligence services, digital humanities, and crisis communication. In future, we will consider relying on cutting-edge technology support and combining it with specific field issues to further enrich the connotations and expand the application boundaries of social-knowledge big graphs.