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Comparison of Vocabulary Features Among Multiple Data Sources for Constructing a Knowledge Base on Disaster Information

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Technologies and Applications of Artificial Intelligence (TAAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2074))

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Abstract

This research aims to develop a framework for smoothly obtaining disaster information from multiple web services through a knowledge base of disaster information. In Japan, where natural disasters occur frequently, there is a need for a system that can utilize disaster information transmitted on the Web from various locations in disaster-stricken areas for rescue operations and disaster recovery when a disaster occurs. Since such information is posted to many web services, searchers must refer to multiple web services to obtain the desired infor mation. In this study, we propose understanding the characteristics of disaster information posted on each web service and using them as a guide for searchers to obtain disaster information smoothly. To achieve this goal, we tried to construct a vocabulary set of disaster information by acquiring textual information from two different data sources and using word embedding and clustering. Comparison of the acquired disaster information revealed two points: The composition of dis aster information categories differs among data sources. Even texts in the same category have different characteristics of words depending on the data source.

M. Yasuo and M. Matsushita—These authors contributed equally to this work.

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Notes

  1. 1.

    https://www.biglobe.co.jp/pressroom/release/2011/04/27-1.

  2. 2.

    https://b.hatena.ne.jp/.

  3. 3.

    https://note.com/.

  4. 4.

    https://www.data.jma.go.jp/kumamoto/shosai/kakusyusiryou/20200708kumamoto.pdf.

  5. 5.

    https://mainichi.jp/contents/edu/maisaku/

  6. 6.

    http://www.cl.ecei.tohoku.ac.jp/~m-suzuki/jawikivector.

  7. 7.

    https://github.com/neologd/mecab-ipadic-neologd.

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Kansai University, 2-1-1, Ryozenji-Cho, Takatsuki, Osaka, 569-1095, Japan

    Megumi Yasuo & Mitsunori Matsushita

Authors
  1. Megumi Yasuo
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  2. Mitsunori Matsushita
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Correspondence to Megumi Yasuo .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chao-Yang Lee

  2. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chun-Li Lin

  3. National Yunlin University of Science and Technology, Douliou, Taiwan

    Hsuan-Ting Chang

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Yasuo, M., Matsushita, M. (2024). Comparison of Vocabulary Features Among Multiple Data Sources for Constructing a Knowledge Base on Disaster Information. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_10

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  • DOI: https://doi.org/10.1007/978-981-97-1711-8_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1710-1

  • Online ISBN: 978-981-97-1711-8

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