Abstract: With the widespread use of knowledge graphs, in order to improve the accuracy and efficiency of extracting knowledge data and product data from them, the method and system use a knowledge graph as a data source, and formulates business data extraction and organization rules based on actual business requirements (the extraction rules are the mapping rules in the title, the expression description methods and specification constraints of the design rules are designed by us, and the actual requirements can be filled out by the business demander), and support the extraction of subgraphs that meet the rules from the knowledge graph according to the rules. Because the subgraph conforms to the rules of the business demander, the subgraph contains the data and organizational structure that meet the business requirements. Further, through data product generation rules (generating data products that are ultimately required by business users, such as report files and statistical tables, from subgraph data with relatively fixed structure and actual business meaning), generate the required data products from the extracted subgraphs ( report documents, statistical tables, etc). Rapid and automatic generation of data products such as text, charts, and report documents are achieved by using SPARQL query language, natural language generation, and other technologies to use knowledge graphs as data sources, which has substantially improved efficiency.

Key words: knowledge graph, ontology, SPARQL query language, generation rule, natural language generation