Knowledge graph of alpine skiing events: A focus on meteorological conditions

• Published in: PloS one Vol. 17; no. 9; p. e0274164
• Main Authors: Tang, Wei, Zhang, Xueying, Feng, Deen, Wang, Yipeng, Ye, Peng, Qu, Hanhua
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 21.09.2022; Public Library of Science (PLoS)
• Subjects: 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; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0274164

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.