HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks

Qiqi Wang; Guanjin Wang; Yihong Pan; Zhipeng Lin; Huijia Li; Qian Liu; Kaiqi Zhao

doi:10.1145/3746252.3761638

HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks

Qiqi Wang
, Guanjin Wang
, Yihong Pan
, Zhipeng Lin
, Huijia Li
, Qian Liu^*
, Kaiqi Zhao^*

^*Corresponding author for this work

Nankai University
The University of Auckland
Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.

Original language	English
Title of host publication	CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery, Inc
Pages	6549-6553
Number of pages	5
ISBN (Electronic)	9798400720406
DOIs	https://doi.org/10.1145/3746252.3761638
State	Published - 10 Nov 2025
Externally published	Yes
Event	34th ACM International Conference on Information and Knowledge Management, CIKM 2025 - Seoul, Korea, Republic of Duration: 10 Nov 2025 → 14 Nov 2025

Publication series

Name	CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management

Conference

Conference	34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Country/Territory	Korea, Republic of
City	Seoul
Period	10/11/25 → 14/11/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

urban knowledge graph
urban spatial tasks

Access to Document

10.1145/3746252.3761638

Cite this

Wang, Q., Wang, G., Pan, Y., Lin, Z., Li, H., Liu, Q., & Zhao, K. (2025). HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks. In CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 6549-6553). (CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management). Association for Computing Machinery, Inc. https://doi.org/10.1145/3746252.3761638

Wang, Qiqi ; Wang, Guanjin ; Pan, Yihong et al. / HUSK : A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks. CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2025. pp. 6549-6553 (CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management).

@inproceedings{dc18f1bc086445988f4993277b7bba98,

title = "HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks",

abstract = "Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.",

keywords = "urban knowledge graph, urban spatial tasks",

author = "Qiqi Wang and Guanjin Wang and Yihong Pan and Zhipeng Lin and Huijia Li and Qian Liu and Kaiqi Zhao",

note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 34th ACM International Conference on Information and Knowledge Management, CIKM 2025 ; Conference date: 10-11-2025 Through 14-11-2025",

year = "2025",

month = nov,

day = "10",

doi = "10.1145/3746252.3761638",

language = "英语",

series = "CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management",

publisher = "Association for Computing Machinery, Inc",

pages = "6549--6553",

booktitle = "CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management",

}

Wang, Q, Wang, G, Pan, Y, Lin, Z, Li, H, Liu, Q & Zhao, K 2025, HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks. in CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management. CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, Inc, pp. 6549-6553, 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Korea, Republic of, 10/11/25. https://doi.org/10.1145/3746252.3761638

HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks. / Wang, Qiqi; Wang, Guanjin; Pan, Yihong et al.
CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2025. p. 6549-6553 (CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - HUSK

T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025

AU - Wang, Qiqi

AU - Wang, Guanjin

AU - Pan, Yihong

AU - Lin, Zhipeng

AU - Li, Huijia

AU - Liu, Qian

AU - Zhao, Kaiqi

PY - 2025/11/10

Y1 - 2025/11/10

N2 - Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.

AB - Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.

KW - urban knowledge graph

KW - urban spatial tasks

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DO - 10.1145/3746252.3761638

M3 - 会议稿件

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BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery, Inc

Y2 - 10 November 2025 through 14 November 2025

ER -

Wang Q, Wang G, Pan Y, Lin Z, Li H, Liu Q et al. HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks. In CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc. 2025. p. 6549-6553. (CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management). doi: 10.1145/3746252.3761638

HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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