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GeoKG'2022 Workshop Report: The 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs

Published: 07 November 2023 Publication History

Abstract

The topic of knowledge graphs (KGs) has recently attracted extensive attention in both industry and academia. Knowledge graphs are a new paradigm for representing, retrieving, integrating, and reasoning data from highly heterogeneous and multimodal sources. For example, international conferences, such as the Knowledge Graph Conference, have emerged in the past years, not to mention the increasing number of specialized workshops on KGs co-located with major computer science conferences, including Knowledge Graph Workshop at KDD 2021, Workshop for Deep Learning in Knowledge Graphs at ISWC 2021, Workshop on Knowledge Graph Construction at ESWC 2021, to name but a few. Also, the number of KGs-related papers accepted at these major conferences, including SIGSPATIAL, is rapidly increasing. Meanwhile, we also witness the increasing popularity of knowledge graph technologies in geography, geoinformatics, and GIScience domains. There are growing numbers of knowledge graph-related manuscripts accepted to the top geospatial-related venues, such as the International Journal of Geographical Information Science, Transactions in GIS, the International Journal of Applied Earth Observation and Geoinformation, and so on. Transactions in GIS also held two special issues about knowledge graphs: 1) Symbolic and Subsymbolic GeoAI: Geospatial Knowledge Graphs and Spatially Explicit Machine Learning [4], and 2) Knowledge-based GIS (K-GIS): Theories, Techniques and Applications. In addition, government agencies, industries, and non-governmental organizations (NGOs) are also lifting resources on topics of exploring KGs to build up interdisciplinary science and applications. One example is NSF's Accelerate Convergence Program, which promotes the idea of building an Open Knowledge Network to harness the data revolution. Funded by this program, the KnowWhereGraph is presently among the largest geo-enabled knowledge graphs, which integrates 28 different data layers at the intersection between humans and their environment [2].

References

[1]
L. Cadorel, A. G. B. Tettamanzi, and F. Gandon. Towards a representation of uncertain geospatial information in knowledge graphs. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs, GeoKG '22, page 1--2, New York, NY, USA, 2022. Association for Computing Machinery.
[2]
K. Janowicz, P. Hitzler, W. Li, D. Rehberger, M. Schildhauer, R. Zhu, C. Shimizu, C. K. Fisher, L. Cai, G. Mai, et al. Know, know where, knowwheregraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence. AI Magazine, 43(1):30--39, 2022.
[3]
Y. Liu, J. Ding, and Y. Li. Developing knowledge graph based system for urban computing. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs, GeoKG '22, page 3--7, New York, NY, USA, 2022. Association for Computing Machinery.
[4]
G. Mai, Y. Hu, S. Gao, L. Cai, B. Martins, J. Scholz, J. Gao, and K. Janowicz. Symbolic and sub-symbolic geoai: Geospatial knowledge graphs and spatially explicit machine learning. Transactions in GIS, 2022.
[5]
J. Rao, S. Gao, M. Miller, and A. Morales. Measuring network resilience via geospatial knowledge graph: A case study of the us multi-commodity flow network. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs, GeoKG '22, page 17--25, New York, NY, USA, 2022. Association for Computing Machinery.
[6]
A. Soni and S. Boddhu. Finding map feature correspondences in heterogeneous geospatial datasets. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs, GeoKG '22, page 7--16, New York, NY, USA, 2022. Association for Computing Machinery.

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Published In

cover image SIGSPATIAL Special
SIGSPATIAL Special  Volume 14, Issue 1
November 2022
55 pages
EISSN:1946-7729
DOI:10.1145/3632268
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2023
Published in SIGSPATIAL Volume 14, Issue 1

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