How to deal with the complexity of large-scale entity relationships when integrating knowledge graphs?

When dealing with the complexity of large-scale entity relationships in knowledge graph integration, it is usually necessary to address it through the coordination of hierarchical modeling, relationship standardization, and incremental update strategies. Hierarchical modeling: Divide the knowledge graph by business domain or entity level, such as treating "products", "users", and "orders" as independent subgraphs to reduce cross-domain relationship coupling. Relationship type standardization: Sort out core relationships (such as "belong to", "associate with", "depend on"), merge redundant relationship types, and reduce semantic ambiguity. Incremental update mechanism: Prioritize processing high-frequency entities and core relationships, dynamically update edge entities through stream processing, and avoid full data recalculation. It is recommended to start with core business entities (such as users, core products) and high-frequency relationships (such as "purchase", "use"), and gradually expand to secondary entities, while combining automated relationship extraction tools to improve processing efficiency. Attention can be paid to knowledge graph incremental update strategies to balance complexity and real-time performance.