Knowledge graph construction method and system based on multi-source database

Abstract

The invention provides a knowledge graph construction method and system based on a multi-source database, and the method comprises the steps: obtaining all tables in each source database, and taking each table as metadata; obtaining a field extraction instruction, extracting the field of each table, and taking the field as the field of the corresponding metadata; for any two pieces of metadata, extracting fields with a matching relationship, traversing entities in the metadata, and constructing all entity relationships between the two pieces of metadata based on the fields with the matching relationship; and taking all the entities as nodes of the knowledge graph and the entity relationships as edges of the knowledge graph to obtain the knowledge graph. The establishment process of the knowledge graph is simplified, and the establishment efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of knowledge graph construction, and in particular relates to a multi-source database-based knowledge graph construction method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] In the face of massive data, traditional relational databases can no longer meet the needs of analysis. Distributed graph databases can clearly reveal various complex relationships, especially for industries such as intricate social networks, logistics, and financial risk control. as obvious. [0004] However, when constructing a knowledge graph traditionally, it is necessary to manually define the source data, and then perform multiple steps such as governance and extraction of data from different sources. The process is relatively cumbersome; in the case of multiple data resources, som...

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Problems solved by technology

[0004] However, when constructing a knowledge graph traditionally, it is necessary to manually define the source data, and then perform multiple steps such as governance and extraction of data from different sources. The process is relatively cumbersome; in the case of multiple data resources, some departments have the same data. However, due to the authority of the department, the data seen by each department is limited, and it is impossible for different departments to build different knowledge graphs and organize the same data; in the case of multi-source data sources, the original data must be guaranteed The structure and data are not destroyed, and the fields in the metadata in the knowledge map are further standardized. Using the traditional method, it is necessary to manually standardize the fields in each table, and input the normalized fields into the dictionary configuration table at the specified location. In the process of manual construction, problems such as manual errors and wrong table configurations are prone to occur; when the multi-source data is in a disordered and complicated format, the form is not uniform, and the total amount of various types of data is large, various problems will occur when processing data synchronization , resulting in failure to build the knowledge map

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