Geospatial Knowledge Hypercube
Authors: 
Zhaonan Wang, Bowen Jin, Wei Hu, Minhao Jiang, Seungyeon Kang, Zhiyuan Li, Sizhe Zhou, Jiawei Han, Shaowen WangAuthors Info & Claims
Zhaonan Wang, Bowen Jin, Wei Hu, Minhao Jiang, Seungyeon Kang, Zhiyuan Li, Sizhe Zhou, Jiawei Han, Shaowen WangAuthors Info & ClaimsArticle No.: 79, Pages 1 - 4
Abstract
Today a tremendous amount of geospatial knowledge is hidden in massive volumes of text data. To facilitate flexible and powerful geospatial analysis and applications, we introduce a new architecture: geospatial knowledge hypercube, a multi-scale, multidimensional knowledge structure that integrates information from geospatial dimensions, thematic themes and diverse application semantics, extracted and computed from spatial-related text data. To construct such a knowledge hypercube, weakly supervised language models are leveraged for automatic, dynamic and incremental extraction of heterogeneous geospatial data, thematic themes, latent connections and relationships, and application semantics, through combining a variety of information from unstructured text, structured tables, and maps. The hypercube lays a foundation for many knowledge discovery and in-depth spatial analysis, and other advanced applications. We have deployed a prototype web application of proposed geospatial knowledge hypercube for public access at: https://hcwebapp.cigi.illinois.edu/.
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Index Terms
- Geospatial Knowledge Hypercube
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Information & Contributors
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Published In
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
- General Chairs:
- Maria Luisa Damiani,
- Matthias Renz,
- Program Chairs:
- Ahmed Eldawy,
- Peer Kröger,
- Mario A Nascimento
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Published: 22 December 2023
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SIGSPATIAL '23
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SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems
November 13 - 16, 2023
Hamburg, Germany
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