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TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based

Yongmei Wang1, Peng Wang1, Yongheng Zhang1

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A new graph database integrates tea risk substances and research, mapping over 4000 nodes and 9000 correlations. This resource aids understanding of tea contaminants and related studies for improved safety.

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Area of Science:

  • Food Science
  • Toxicology
  • Bioinformatics

Background:

  • Tea production can introduce harmful substances, but data on these contaminants and their research links are fragmented.
  • Existing literature searches struggle to systematically identify and correlate tea risk substances and associated research.

Purpose of the Study:

  • To construct a novel knowledge graph database for integrating and analyzing tea risk substances and their research relationships.
  • To provide a comprehensive resource for understanding contaminants in tea and their scientific investigation.

Main Methods:

  • Developed a Neo4j graph database using knowledge mapping techniques.
  • Integrated data on 4189 nodes and 9400 correlations, linking risk substances, research categories, and publications (PMIDs).
  • Included nine main types of tea risk substances and six types of tea research papers.

Main Results:

  • The database is the first of its kind specifically for tea risk substances and related research.
  • It contains extensive data on various contaminants like heavy metals, pesticides, and microorganisms.
  • The graph structure facilitates exploration of connections between substances and research areas.

Conclusions:

  • This database serves as a crucial reference for investigating the origins of tea risk substances.
  • It will aid in developing future tea safety standards and risk assessments.
  • The resource supports a deeper understanding of tea quality and safety through data integration.