ForestFoodKG: A Structured Dataset and Knowledge Graph for Forest Food Taxonomy and Nutrition

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Abstract
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.
Funding Information
  • The Outstanding Youth Team of Central Universities (QNTD202504)
  • the Fundamental Research Funds for the Central Universities (BLX202356)
  • National Key Research and Development Program of China (2022YFF1302700)
  • The Emergency Open Competition Project of National Forestry and Grassland Administration (202303)
  • Outstanding Youth Team Project of Central Universities (QNTD202308)

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