A Chinese Knowledge Graph Dataset in the Field of Scientific Fitness - Shutong Du; Zhitong Liu; Bingyu Pan (Scientific Data)
Abstract To promote the development of scientific fitness research and practice, we propose the Chinese Knowledge Graph Dataset in the Field of Scientific Fitness (FitKG-CN). This knowledge graph contains over 10,000 fitness-related terms, categorized into eight main groups: body parts, items of exercise, fitness movement, equipment and tools, exercise goals, anatomical structures, nutrients, and technical terms. The construction of FitKG-CN is based on authoritative data sources, undergoing rigorous preprocessing, including noise removal, format standardization, and normalization of entities and relationships. The data is manually annotated on a professional platform and ultimately stored in a Neo4j graph database for visualization. Additionally, we trained a Chinese SpERT model using the manually annotated data to enhance the automation of data processing. The experimental results show that the model achieved an F1 score of 94.05% in entity recognition tasks and 82.00% in relation extraction tasks, validating the effectiveness of the model and improving the scalability of the dataset.