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Comparing Spatial-Temporal Knowledge Graph on Spatial Downstream Tasks

Published: 22 November 2024 Publication History

Abstract

Knowledge graphs have become a universal data representation and integration mechanism. They recently gained interest in the spatial area. Spatial-Temporal Knowledge Graphs (STKGs) in particular have been created to integrate diverse sets of spatial data and model the relationships of spatial entities. Public knowledge graphs, such as KnowWhereGraph and WorldKG, have received a high traction in the domain of STKGs. In this paper, we compare three STKGs using downstream tasks within the Spatio-Temporal domain, and also discuss the underlying modeling decisions. We conduct an evaluation on a wildfire dataset and a housing dataset, comparing different embedding methodologies for the different knowledge graphs. We show that modeling paradigms in STKGs as well as algorithmic choices can have an impact on the downstream performance, and discuss challenges in both areas.

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cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 22 November 2024

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Author Tags

  1. Knowledge Graph embeddings
  2. Price prediction
  3. Spatial-Temportal Knowledge Graph
  4. Wildfire prediction

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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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