Knowledge Graph-driven Weather Overview Generation for the Beijing 2022 Winter Olympic Games (2024)

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  Weather Overview is regarded as one of the crucial meteorological services supporting the Beijing 2022 Olympic and Paralympic Winter Games (Beijing 2022). Due to its characteristics of multiple-data, large-scale, and vulnerability to weather conditions, there still exist quite a few challenging issues in obtaining Weather Overview. At present, the knowledge graph (KG) is believed to be an effective way to describe information and knowledge. Thus, this research focuses on the development of a framework for automatic generation of Weather Overview using KG. We first present a three-layer KG model to generate accurate content of Weather Overview: (1) knowledge acquisition of entities and relationships to construct the specific corpora; (2) knowledge representation of the relationships between weather conditions and events based on ontology; (3) knowledge application of corpora, variables, and weather conditions to query and reason knowledge with Neo4j. Moreover, an XML Schema is used to achieve the standardized Weather Overview which is formed by sentence-paragraph-text generation. This model is validated in the typical case of Yanqing National Alpine Skiing Centre in Beijing 2022. Compared to the manual method, the accuracy and standardization of Weather Overview can be maintained above 90%, and able to automatically generate within seconds. The method proposed in this study provides a solid meteorological service solution for other large-scale sports events.

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