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BASIL DB: bioactive semantic integration and linking database.

David Jackson1, Paul Groth2, Hazar Harmouch2

  • 1University of Amsterdam, Amsterdam, The Netherlands. d.i.jackson@uva.nl.

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|August 13, 2025
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Summary
This summary is machine-generated.

BASIL DB is a new knowledge graph database that uses natural language processing to organize bioactive compounds, foods, and health effects. This resource enhances research efficiency and insight discovery for personalized nutrition and disease prevention.

Keywords:
Bioactive compoundsClinical trialsData integrationEvidence-based healthKnowledge graphNatural language processing (NLP)PubMed

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Area of Science:

  • Nutritional Science
  • Bioinformatics
  • Computational Biology

Background:

  • Bioactive compounds in foods and plants offer health benefits like antioxidant and anti-inflammatory effects.
  • Research in personalized nutrition and disease prevention is expanding but faces challenges with data complexity and literature growth.
  • The BioActive Semantic Integration and Linking Database (BASIL DB) was developed to address these challenges using a knowledge graph approach.

Purpose of the Study:

  • To create a scalable and comprehensive knowledge graph database for bioactive compounds.
  • To streamline the organization and analysis of data on bioactive compounds, foods, and their health impacts.
  • To facilitate research into the role of bioactive compounds in disease prevention and personalized nutrition.

Main Methods:

  • Data collection from structured databases and PubMed for randomized controlled trials (RCTs).
  • Data preprocessing involving cleaning inconsistencies and structuring data.
  • Utilizing natural language processing (NLP) tools, including a large language model (LLM), for data extraction from clinical trials.
  • Integration of extracted data into a knowledge graph with Foods, Bioactives, and Health Conditions as nodes and their interactions as weighted edges.

Main Results:

  • The BASIL DB contains 433 compounds, 40,296 research papers, 7,256 health effects, and 4,197 food items.
  • The database provides query and visualization capabilities, including interactive graphs and custom filtering.
  • Users can explore relationships between bioactives and health effects, improving research efficiency.

Conclusions:

  • BASIL DB is a structured knowledge graph resource for exploring relationships among bioactives, foods, and health outcomes.
  • It represents a step towards a systematic, data-driven approach to understanding bioactive compound health effects.
  • Future work includes database expansion and method refinement to bridge traditional and conventional nutrition approaches.