Methods, devices, electronic equipment, and storage media for constructing urban knowledge graphs

CN117556053BActive Publication Date: 2026-05-26HONG KONG UNIV OF SCI & TECH (GUANGZHOU)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HONG KONG UNIV OF SCI & TECH (GUANGZHOU)
Filing Date
2023-11-02
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing methods for constructing urban knowledge graphs are only applicable to specific application scenarios, neglecting the mining of key urban entities and relationships, failing to reflect the hierarchical structure of the city, and leading to unreasonable urban planning.

Method used

Multiple sub-regions of the target city are extracted into multiple entities and divided into multiple levels according to regional functions. A structural relationship diagram is constructed for each level. The structural relationship diagrams of different levels are connected through the membership relationships of entities and categories to form a city knowledge graph with a multi-level structure.

Benefits of technology

It realizes a multi-level structure of urban knowledge graph, which can reflect the hierarchical structure of the city, improve the rationality of urban planning, and provide high-level urban knowledge to support decision-making.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application discloses a method, apparatus, electronic device, and storage medium for constructing a city knowledge graph. The method includes: extracting multiple sub-regions of a target city into multiple entities; dividing the multiple entities into multiple levels according to the regional function corresponding to each sub-region; constructing a structural relationship diagram for each level based on the spatial positional relationships of the multiple entities contained in each level; and connecting the entities in different structural relationship diagrams according to the spatial affiliation relationships of entities in different levels to obtain the city knowledge graph of the target city. This application can construct a city knowledge graph with a multi-level structure, which can reflect the hierarchical structure of the city. Furthermore, city decision-makers can obtain higher-level urban knowledge through this city knowledge graph, improving the rationality of urban planning.
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