A knowledge graph generation method based on vector database

By identifying and quantizing entities based on the BERT-Base language model and GloVe word embedding technology, and combining adaptive entity embedding adjustment and graph attention relationship reasoning mechanism, the entity representation in the vector database is optimized, solving the problem of low data processing efficiency, realizing real-time updates and revealing complex relationships, and improving the accuracy and analytical capabilities of the knowledge graph.

CN120930761BActive Publication Date: 2026-05-26CHINA NAT BUILDING MATERIALS TECH CO LTD +2
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Patent Information

Application Number
CN202410781479.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-06-17
Publication Date
2026-05-26
Estimated Expiration
2044-06-17

AI Technical Summary

Technical Problem

Existing knowledge graph generation methods suffer from low data processing efficiency, prolonged response time, and delayed knowledge graph updates when processing data in vector databases due to the high dimensionality and query complexity of vector databases.

Method used

By collecting knowledge data from social media, professional articles, and databases, we use the BERT-Base language model and GloVe word embedding technology to identify and quantize entities. We then combine adaptive entity embedding adjustment technology and graph attention-based relational reasoning mechanism to optimize entity vector representation and generate a knowledge graph, which is then stored in a vector database.

Benefits of technology

It enables real-time updating and adjustment of entity vectors, improving data accuracy and response speed, revealing and strengthening complex relationships between entities, and enhancing the semantic richness and advanced analytical capabilities of the knowledge graph.

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Abstract

This invention relates to the field of knowledge graph generation technology, specifically to a knowledge graph generation method based on a vector database. It includes the following steps: S1, collecting knowledge data and preprocessing the data; S2, identifying and quantizing entities in the text data using the BERT-Base language model and GloVe word embedding technology, and storing them in a vector database; S3, updating and adjusting the entity vectors stored in the vector database in real time using adaptive entity embedding adjustment technology; S4, analyzing and learning the vector representations of entities and their relationships using a graph attention-based relational reasoning mechanism to generate a knowledge graph; S5, storing the knowledge graph in the vector database. This knowledge graph generation method based on a vector database, through adaptive entity embedding adjustment technology and a graph attention-based relational reasoning mechanism, achieves knowledge graph generation and real-time updating of entity vectors, improving the accuracy of the knowledge graph.
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