Fig. 1: Construction process of a knowledge graph for ancient Chinese costumes.

Note: This diagram illustrates the comprehensive process of constructing a knowledge graph. The process begins with data collection, where information is gathered from both offline sources, such as books, and online platforms, including Baidu Baike and museum databases. The knowledge construction process is divided into three key stages: knowledge extraction, knowledge fusion, and knowledge storage and graph construction. During the knowledge extraction phase, entity extraction identifies key elements such as names, categories, and types; attribute extraction focuses on capturing functional and structural characteristics; and relation extraction maps the semantic relationships between entities, such as “belongs to” or “consists of”. In the knowledge fusion phase, techniques such as entity detection, coreference resolution (e.g., unifying various expressions of the same entity), and entity disambiguation (e.g., clarifying ambiguities between similar terms) are employed to enhance the consistency and accuracy of the data. The diagram employs the example of the “Liuhe Tongyi Hat <六合统一帽>“ from the Qing Dynasty to demonstrate the fusion processes. Finally, the processed knowledge is stored in a Neo4j graph database, where entities and their relationships are visualized in a structured graph format.