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Design Considerations for a Knowledge Graph: The WATRIMed Use Case.

Borlli Michel Jonas Some1, Georgeta Bordea2, Frantz Thiessard2

  • 1Ecole Supérieure d'Informatique, Univ. Nazi Boni, Bobo Dioulasso, Burkina Faso.

Studies in Health Technology and Informatics
|March 30, 2019
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Summary
This summary is machine-generated.

This study introduces the West African Herbal Traditional Medicine Knowledge Graph (WATRIMed KG), a digital resource for West African herbal medicine. It aims to standardize and integrate traditional knowledge with conventional medicine for better data management.

Keywords:
Knowledge GraphMedical Knowledge RepresentationPlant based Traditional MedicineUpper Level Ontology

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Area of Science:

  • Ethnobotany
  • Computational Linguistics
  • Bioinformatics

Background:

  • Traditional Medicine (TM), particularly herbal TM, is widely used globally, but lacks standardization and computable data.
  • Oral transmission of TM knowledge hinders its integration with modern scientific practices.
  • There is a need for shareable standards to enable computational management of TM data.

Purpose of the Study:

  • To design and develop the West African Herbal Traditional Medicine (WATRIMed) Knowledge Graph (KG).
  • To digitize West African TM and create a bridge to conventional medicine.
  • To establish computable standards for TM data management.

Main Methods:

  • Development of the WATRIMed Knowledge Graph (KG).
  • Enrichment of KG entities with external knowledge bases.
  • Mapping KG entities with the BioTopLite Upper Level Ontology.

Main Results:

  • The WATRIMed KG model includes 472 Concepts and 75 Properties.
  • The KG formally describes 115 medicinal plants, 179 chemical compounds, and 67 recipes.
  • The KG provides a structured, computable representation of West African herbal medicine.

Conclusions:

  • The WATRIMed KG represents a significant step towards digitizing and standardizing West African Traditional Medicine.
  • This initiative facilitates the integration of traditional herbal knowledge with conventional medical systems.
  • The developed KG model can support computational data management and further research in ethnopharmacology.