Knowledge graph of alpine skiing events: A focus on meteorological conditions
Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most adv...
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Published in | PloS one Vol. 17; no. 9; p. e0274164 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Public Library of Science
21.09.2022
Public Library of Science (PLoS) |
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Abstract | Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events. |
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AbstractList | Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events. |
Audience | Academic |
Author | Zhang, Xueying Wang, Yipeng Qu, Hanhua Ye, Peng Tang, Wei Feng, Deen |
AuthorAffiliation | 1 Public Meteorological Service Center, China Meteorological Administration, Beijing, China 3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China 4 Urban Planning and Development Institute, Yangzhou University, Yangzhou, China 5 College of Architectural Science and Engineering, Yangzhou University, Yangzhou, China 2 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China University of Innsbruck: Universitat Innsbruck, AUSTRIA |
AuthorAffiliation_xml | – name: University of Innsbruck: Universitat Innsbruck, AUSTRIA – name: 4 Urban Planning and Development Institute, Yangzhou University, Yangzhou, China – name: 2 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China – name: 5 College of Architectural Science and Engineering, Yangzhou University, Yangzhou, China – name: 1 Public Meteorological Service Center, China Meteorological Administration, Beijing, China – name: 3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China |
Author_xml | – sequence: 1 fullname: Tang, Wei – sequence: 2 fullname: Zhang, Xueying – sequence: 3 fullname: Feng, Deen – sequence: 4 fullname: Wang, Yipeng – sequence: 5 fullname: Ye, Peng – sequence: 6 fullname: Qu, Hanhua |
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Cites_doi | 10.1145/1376616.1376746 10.1016/j.eswa.2019.112948 10.1016/j.jort.2017.09.002 10.1055/s-2007-1024919 10.1145/2556195.2556250 10.1007/s11704-016-5228-9 10.1007/978-3-030-59028-4_10 10.3233/SW-160218 10.1177/0047287508321207 10.1136/bjsports-2017-098864 10.1109/SANER.2017.7884609 10.1175/2010BAMS2913.1 10.1016/j.cageo.2017.12.007 10.3103/S1068373913100014 10.1007/s10584-017-1992-2 10.1016/j.websem.2009.07.002 10.1080/23311886.2019.1681246 10.1109/TKDE.2017.2754499 |
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Copyright | COPYRIGHT 2022 Public Library of Science 2022 Tang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Tang et al 2022 Tang et al |
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Snippet | Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot... |
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SubjectTerms | Alpine environments Big Data Biology and Life Sciences Computer and Information Sciences Data analysis Decision analysis Decision making Downhill skiing Dynamic characteristics Earth Sciences Environmental aspects Events planning Humidity Influence Knowledge acquisition Knowledge bases (artificial intelligence) Knowledge representation Logic Meteorological conditions Olympic games Ontology Paralympic Games Precipitation Resource Description Framework-RDF Risk management Semantic web Semantics Skiing Snow Social Sciences Weather |
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Title | Knowledge graph of alpine skiing events: A focus on meteorological conditions |
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