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Wildfire prediction for California using and comparing Spatio-Temporal Knowledge Graphs

  • Martin Böckling

    Martin Böckling studied Business Informatic at the University of Mannheim. He is currently a PhD student at the chair of Prof. Dr. Heiko Paulheim, where he focuses his research on Spatio-Temporal Knowledge Graphs and its usage in prediction tasks. During his master studies, he was involved in several research projects exploring the usage of knowledge graphs in different research domains. For his Bachelor and Master studies, he was participated in a study program within SAP.

     EMAIL logo     , Heiko Paulheim

    Prof. Dr. Heiko Paulheim is a full professor for data science at the University of Mannheim. He holds a PhD from the Technical University of Darmstadt and has conducted research at the University of Applied Sciences of Darmstadt, SAP Research, and the Technical University of Darmstadt prior to this position. His group conducts various projects around knowledge graphs, yielding, among others, the public knowledge graphs WebIsALOD, CaLiGraph, and DBkWik. Moreover, his group is concerned with using knowledge graphs in machine learning, which has lead to the development of the widespread RDF2vec method for knowledge graph embeddings. In the recent past, Heiko Paulheim also leads projects which are concerned with ethical, societal, and legal aspects of AI, such as price-setting AIs on antitrust legislation, ethical news recommenders, and the analysis of hate speech in social media.

         und Sarah Detzler

    Dr. Sarah Detzler is the Competence Lead for Data Science and Machine Learning at SAP. She holds a PhD from KIT and was part of the research group for efficient algorithms focusing on evolutionary algorithms. In her PhD research she focused on the algorithmic optimization of charging networks for electric cars using factos as the price together with fixed boundary conditions. Besides her PhD degree, she holds a Bachelor and Master degree in both mathematics and physics.
Veröffentlicht/Copyright: 9. November 2023
it - Information Technology
Aus der Zeitschrift it - Information Technology Band 65 Heft 4-5

Abstract

The frequency of wildfires increases yearly and poses a constant threat to the environment and human beings. Different factors, for example surrounding infrastructure to an area (e.g., campfire sites or power lines) contribute to the occurrence of wildfires. In this paper, we propose using a Spatio-Temporal Knowledge Graph (STKG) based on OpenStreetMap (OSM) data for modeling such infrastructure. Based on that knowledge graph, we use the RDF2vec approach to create embeddings for predicting wildfires, and we align different vector spaces generated at each temporal step by partial rotation. In an experimental study, we determine the effect of the surrounding infrastructure by comparing different data composition strategies, which involve a prediction based on tabular data, a combination of tabular data and embeddings, and solely embeddings. We show that the incorporation of the STKG increases the prediction quality of wildfires.

Keywords: knowledge graph embeddings; Spatio-Temporal Knowledge Graph; RDF2vec; wildfire prediction
ACM CCS: Applied computing → Physical sciences and engineering → Earth and atmospheric sciences; Information systems → Information systems application → Spatial-temporal systems; Computing methodologies → Machine Learning
Corresponding author: Martin Böckling, University of Mannheim, Data and Web Science Group, B6, 26, 68159 Mannheim, Germany, E-mail:

About the authors

Martin Böckling

Martin Böckling studied Business Informatic at the University of Mannheim. He is currently a PhD student at the chair of Prof. Dr. Heiko Paulheim, where he focuses his research on Spatio-Temporal Knowledge Graphs and its usage in prediction tasks. During his master studies, he was involved in several research projects exploring the usage of knowledge graphs in different research domains. For his Bachelor and Master studies, he was participated in a study program within SAP.

Heiko Paulheim

Prof. Dr. Heiko Paulheim is a full professor for data science at the University of Mannheim. He holds a PhD from the Technical University of Darmstadt and has conducted research at the University of Applied Sciences of Darmstadt, SAP Research, and the Technical University of Darmstadt prior to this position. His group conducts various projects around knowledge graphs, yielding, among others, the public knowledge graphs WebIsALOD, CaLiGraph, and DBkWik. Moreover, his group is concerned with using knowledge graphs in machine learning, which has lead to the development of the widespread RDF2vec method for knowledge graph embeddings. In the recent past, Heiko Paulheim also leads projects which are concerned with ethical, societal, and legal aspects of AI, such as price-setting AIs on antitrust legislation, ethical news recommenders, and the analysis of hate speech in social media.

Sarah Detzler

Dr. Sarah Detzler is the Competence Lead for Data Science and Machine Learning at SAP. She holds a PhD from KIT and was part of the research group for efficient algorithms focusing on evolutionary algorithms. In her PhD research she focused on the algorithmic optimization of charging networks for electric cars using factos as the price together with fixed boundary conditions. Besides her PhD degree, she holds a Bachelor and Master degree in both mathematics and physics.

Acknowledgments

Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-06-30
Accepted: 2023-10-10
Published Online: 2023-11-09
Published in Print: 2023-08-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Machine learning applications
  4. Contributions to a thematic issue
  5. Machine learning and cyber security
  6. Artificial intelligence for molecular communication
  7. Machine learning in run-time control of multicore processor systems
  8. Machine learning in sensor identification for industrial systems
  9. Wildfire prediction for California using and comparing Spatio-Temporal Knowledge Graphs
  10. Machine learning in computational literary studies
  11. Machine learning in AI Factories – five theses for developing, managing and maintaining data-driven artificial intelligence at large scale
https://doi.org/10.1515/itit-2023-0061
Schlagwörter für diesen Artikel
knowledge graph embeddings; Spatio-Temporal Knowledge Graph; RDF2vec; wildfire prediction

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Machine learning applications
  4. Contributions to a thematic issue
  5. Machine learning and cyber security
  6. Artificial intelligence for molecular communication
  7. Machine learning in run-time control of multicore processor systems
  8. Machine learning in sensor identification for industrial systems
  9. Wildfire prediction for California using and comparing Spatio-Temporal Knowledge Graphs
  10. Machine learning in computational literary studies
  11. Machine learning in AI Factories – five theses for developing, managing and maintaining data-driven artificial intelligence at large scale
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