TY - GEN
T1 - Geospatial Knowledge Hypercube (Demo Paper)
AU - Wang, Zhaonan
AU - Jin, Bowen
AU - Hu, Wei
AU - Jiang, Minhao
AU - Kang, Seungyeon
AU - Li, Zhiyuan
AU - Zhou, Sizhe
AU - Han, Jiawei
AU - Wang, Shaowen
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/11/13
Y1 - 2023/11/13
N2 - 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/.
AB - 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/.
KW - geographic information retrieval
KW - knowledge hypercube
KW - weakly-supervised text classification
UR - http://www.scopus.com/inward/record.url?scp=85182503128&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182503128&partnerID=8YFLogxK
U2 - 10.1145/3589132.3625629
DO - 10.1145/3589132.3625629
M3 - Conference contribution
AN - SCOPUS:85182503128
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
A2 - Damiani, Maria Luisa
A2 - Renz, Matthias
A2 - Eldawy, Ahmed
A2 - Kroger, Peer
A2 - Nascimento, Mario A.
PB - Association for Computing Machinery
T2 - 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2023
Y2 - 13 November 2023 through 16 November 2023
ER -