A Chinese Knowledge Graph Dataset in the Field of Scientific Fitness

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AG8WCI8Y6" target="_blank" >RIV/00216208:11320/26:G8WCI8Y6 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41597-025-04519-6" target="_blank" >https://www.nature.com/articles/s41597-025-04519-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41597-025-04519-6" target="_blank" >10.1038/s41597-025-04519-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Chinese Knowledge Graph Dataset in the Field of Scientific Fitness

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Chinese Knowledge Graph Dataset in the Field of Scientific Fitness

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Data

ISSN

2052-4463

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

205

Kód UT WoS článku

001413333200005

EID výsledku v databázi Scopus

2-s2.0-85217989010