Intelligent agent query method and device based on bimodal semantics and storage medium

By converting query questions into question vectors, identifying suitable target databases, and generating query statements, the problem of low query efficiency in existing technologies is solved. This enables interoperability between RDF databases and graph databases, improving query efficiency and ease of use.

CN122262279APending Publication Date: 2026-06-23PERSAGY TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610335395.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-18
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

In existing technologies, data queries based on knowledge graphs can usually only target one of RDF or graph databases, resulting in low query efficiency and an inability to simultaneously satisfy semantic logical reasoning capabilities and high-efficiency graph structure query capabilities.

Method used

The query question is converted into a question vector. Based on the question vector, the target database is determined from the RDF database and the graph database, and a matching query statement is generated to achieve interoperability between the RDF database and the graph database. The query is performed using bimodal semantics.

Benefits of technology

It improves the efficiency and ease of use of data querying. By automatically generating query statements, users do not need to have knowledge of database query syntax. It also enables interoperability between RDF databases and graph databases, improving the comprehensiveness and accuracy of queries.

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Abstract

The invention discloses an agent query method and device based on bimodal semanteme and a storage medium, and relates to the technical field of data query based on artificial intelligence, and the method comprises the following steps: converting a query problem into a problem vector; determining a target database from an RDF database and a graph database according to the problem vector and a preset problem vector library, wherein the RDF database and the graph database are maintained according to the same basic data; and determining a target query statement matched with the type of the target database according to the query problem, and performing query in the target database according to the target query statement to obtain a query result. Intercommunication between the RDF database and the graph database can be realized, and the data query efficiency is improved. The target query statement is generated based on the target database, user operation can be further simplified, the query statement is automatically generated, a user only needs to describe query or analysis requirements through a natural language, the user does not need to have grammatical knowledge of the database query statement, and usability is improved.
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