How to use Schema.org to facilitate the data structuring of knowledge graphs?

When needing to build or optimize a knowledge graph, Schema.org helps convert scattered information into machine-readable structured data by providing standardized data type and attribute definitions, thereby facilitating entity association and relationship construction in the knowledge graph. Specific applications include: - Entity type selection: Choose the corresponding Schema type based on the knowledge graph theme (e.g., Person for individuals, Organization for enterprises, Product for products) to clarify the core entity scope. - Relationship definition: Use Schema properties to describe relationships between entities (e.g., "member" to associate organizations with members, "parentOrganization" to indicate corporate subordination), establishing the association network of the knowledge graph. - Attribute supplementation: Refine entity characteristics through attributes such as "name", "description", and "datePublished", providing rich descriptive information for the knowledge graph. - Format embedding: Embed Schema markup into web pages or datasets using formats like JSON-LD, making it easy for search engines and knowledge graph systems to crawl and parse. It is recommended to start marking from the core entity types of the knowledge graph, prioritize improving high-frequency relationship attributes, and maintain consistency in data formats and attribute values to enhance the accuracy and association efficiency of the knowledge graph.