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

Rongen Yan1,2,3, Zhidan Chen1,2,3, Shengqi Zhou4

  • 1School of Information Science and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, China.

Foods (Basel, Switzerland)
|December 30, 2025
PubMed
Summary

We developed ForestFoodKG, a new knowledge base detailing the taxonomy and nutrition of 1191 forest foods. This resource enhances dietary diversity and sustainable forestry research.

Keywords:
dataset constructionforest foodknowledge graphnutritional informationtaxonomy

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Area of Science:

  • Nutrition Science
  • Ecology
  • Bioinformatics

Background:

  • Forest foods are crucial for dietary diversity, health, and ecosystem sustainability.
  • Existing resources lack structured, machine-readable data on forest food taxonomy and nutrition.

Purpose of the Study:

  • To create a comprehensive, structured knowledge base for forest foods.
  • To integrate taxonomic hierarchy and nutritional composition data for 1191 forest food items.

Main Methods:

  • Developed the ForestFoodKG dataset with standardized taxonomic and nutritional records across seven biological levels.
  • Constructed the ForestFoodKG Knowledge Graph (ForestFoodKG-KG) using named entity recognition and relation extraction to link food entities.
  • Manually verified all data for accuracy.

Main Results:

  • The ForestFoodKG dataset includes 1191 forest food items.
  • The ForestFoodKG-KG comprises 4492 entities and 14,130 semantic relations.
  • Data is publicly available in CSV format on GitHub.

Conclusions:

  • ForestFoodKG is the first structured knowledge base for forest foods.
  • This resource supports intelligent querying, nutrition analytics, and ecological informatics.
  • It promotes data-driven research in nutrition science and sustainable forestry.