Shengqi Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shengqi Zhou has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Fu Xu
Beijing Forestry University
1 shared publication - Guoxing Niu
School of Information Science and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, China
1 shared publication - Jingyi Xu
School of Foreign Languages, Beijing Forestry University, Beijing 100083, China
1 shared publication - Jun Wang
College of Forestry, Beijing Forestry University, Beijing 100083, China
1 shared publication - Keyan Liu
School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
1 shared publication - Qi Luo
School of Information Science and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
ForestFoodKG: A Structured Dataset and Knowledge Graph for Forest Food Taxonomy and Nutrition
Forest foods play a vital role in enhancing dietary diversity, human health, and the sustainable use of forest ecosystems. However, structured and machine-readable resources that systematically describe their taxonomic and nutritional attributes remain scarce. To fill this gap, we introduce ForestFoodKG, a comprehensive resource that integrates taxonomic hierarchy and nutritional composition of 1191 forest food items. The resource consists of two components—(i) the ForestFoodKG dataset, containing standardized taxonomic and nutritional records across seven biological levels, and (ii) the ForestFoodKG Knowledge Graph (ForestFoodKG-KG), which semantically links forest food entities using named entity recognition and relation extraction. The constructed graph comprises 4492 entities and 14,130 semantic relations, providing a structured foundation for intelligent querying, nutrition analytics, and ecological informatics. All data were manually verified and made publicly available in CSV format on GitHub. ForestFoodKG serves as the first structured knowledge base for forest foods, promoting data-driven research in nutrition science, sustainable forestry, and knowledge-based decision-making.
Open AccessForestFoodKG: A Structured Dataset and Knowledge Graph for Forest Food Taxonomy and Nutrition
Forest foods play a vital role in enhancing dietary diversity, human health, and the sustainable use of forest ecosystems. However, structured and machine-readable resources that systematically describe their taxonomic and nutritional attributes remain scarce. To fill this gap, we introduce ForestFoodKG, a comprehensive resource that integrates taxonomic hierarchy and nutritional composition of 1191 forest food items. The resource consists of two components—(i) the ForestFoodKG dataset, containing standardized taxonomic and nutritional records across seven biological levels, and (ii) the ForestFoodKG Knowledge Graph (ForestFoodKG-KG), which semantically links forest food entities using named entity recognition and relation extraction. The constructed graph comprises 4492 entities and 14,130 semantic relations, providing a structured foundation for intelligent querying, nutrition analytics, and ecological informatics. All data were manually verified and made publicly available in CSV format on GitHub. ForestFoodKG serves as the first structured knowledge base for forest foods, promoting data-driven research in nutrition science, sustainable forestry, and knowledge-based decision-making.