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Main Authors: Attar, Manar, Wang, Shuai, Siebes, Ronald, Kultorp, Eirik, Huang, Zhisheng, Lu, Tianyang
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2501.14762
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author Attar, Manar
Wang, Shuai
Siebes, Ronald
Kultorp, Eirik
Huang, Zhisheng
Lu, Tianyang
author_facet Attar, Manar
Wang, Shuai
Siebes, Ronald
Kultorp, Eirik
Huang, Zhisheng
Lu, Tianyang
contents The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. Linked Data technology can be used to unify, enrich, and integrate data from multiple sources. In our work, we focus on datasets about damaging events in Ukraine due to Russia's invasion between February 2022 and the end of April 2023. We convert two selected datasets to Linked Data and enrich them with additional geospatial information. Following that, we present an algorithm for the detection of identical events from different datasets. Our pipeline makes it easy to convert and enrich datasets to integrated Linked Data. The resulting dataset consists of 10K reported events covering damage to hospitals, schools, roads, residential buildings, etc. Finally, we demonstrate in use cases how our dataset can be applied to different scenarios for resilience purposes.
format Preprint
id arxiv_https___arxiv_org_abs_2501_14762
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Linked Data on Geo-annotated Events and Use Cases for the Resilience of Ukraine
Attar, Manar
Wang, Shuai
Siebes, Ronald
Kultorp, Eirik
Huang, Zhisheng
Lu, Tianyang
Computers and Society
Social and Information Networks
E.2; I.7.0; J.0
The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. Linked Data technology can be used to unify, enrich, and integrate data from multiple sources. In our work, we focus on datasets about damaging events in Ukraine due to Russia's invasion between February 2022 and the end of April 2023. We convert two selected datasets to Linked Data and enrich them with additional geospatial information. Following that, we present an algorithm for the detection of identical events from different datasets. Our pipeline makes it easy to convert and enrich datasets to integrated Linked Data. The resulting dataset consists of 10K reported events covering damage to hospitals, schools, roads, residential buildings, etc. Finally, we demonstrate in use cases how our dataset can be applied to different scenarios for resilience purposes.
title Linked Data on Geo-annotated Events and Use Cases for the Resilience of Ukraine
topic Computers and Society
Social and Information Networks
E.2; I.7.0; J.0
url https://arxiv.org/abs/2501.14762