Linking African Traditional Medicine Knowledge
Domaine:
healthcareType de record:
datasetpaper
Créateur:
GosBoeSchGay
Éditeur:
fig
Hôte:
This is an updated version of the paper published at Linking African Traditional Medicine Knowledge A. Gossa Lo, Victor de Boer, Stefan Schlobach in "Semantic Web Applications and Tools for Health Care and Life Sciences". Proceedings of the 10th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4LS 2017) Rome, Italy, December 4-7, 2017.
This updated version has an extended author list an acknowledgements.
African Traditional Medicine (ATM) is widely used in Africa as the first-line of treatment thanks to its accessibility and affordability. However, the lack of formalization of this knowledge can lead to safety issues and malpractice. This paper investigates a possible contribution of the Semantic Web in realizing the formalization and integration of ATM with data on conventional medicine (CM). As a proof of concept we convert various ATM datasets and link them to CM data. This results in a Linked ATM knowledge graph. We finally give some examples with some interesting SPARQL queries and insightful results.
Acknowledgements. This work was supported by ANR ( Grant nANR-16-MRSE-0019-01), IDEX University of Bordeaux and W4RA (w4ra.org). We thank Hospital Traditional Keur Massar, Djibril Ba and Genevi`eve Baumann.
This updated version has an extended author list an acknowledgements.
African Traditional Medicine (ATM) is widely used in Africa as the first-line of treatment thanks to its accessibility and affordability. However, the lack of formalization of this knowledge can lead to safety issues and malpractice. This paper investigates a possible contribution of the Semantic Web in realizing the formalization and integration of ATM with data on conventional medicine (CM). As a proof of concept we convert various ATM datasets and link them to CM data. This results in a Linked ATM knowledge graph. We finally give some examples with some interesting SPARQL queries and insightful results.
Acknowledgements. This work was supported by ANR ( Grant nANR-16-MRSE-0019-01), IDEX University of Bordeaux and W4RA (w4ra.org). We thank Hospital Traditional Keur Massar, Djibril Ba and Genevi`eve Baumann.