African Traditional Medicine (ATM) is widely used in Africa as the rst-line of treatment thanks to its accessibility and a ordability. 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 nally give some examples with some interesting SPARQL queries and insightful results.
1,100,000 items of data, divided over 40 categories. Furthermore, a semantic
eScience infrastructure was established, in response to the sheer volume and
diversity of TCM information and services that a ect its interoperability [
The Linking Open Drug Data (LODD) project aggregated and added
biomedical data (e.g. about drugs, TCM, diseases, etc.) to the Linked Data Cloud. Their
focus is to facilitate the obtaining of new insights and nding unforeseen
associations between entities. The data sets contain over 8.4 million RDF triples and
388,000 RDF links to external data sources [
We investigate the potential of Semantic Web technologies for the preservation and formalization of ATM knowledge through two use cases. The rst concerns the di erence in treatment in two regions in Madagascar. Both the availability of plant species in a region and the knowledge about treatment methods that is passed on in a community in uence treatment practice. Formalizing these methods prevents loss of knowledge and can help ATM practitioners in rural regions to exchange knowledge and nd new treatments. The second use case focuses on investigating di erences between ATM practice in Senegal and Madagascar. This exchange of knowledge not only bene ts ATM practitioners, but could also be a valuable contribution to drug discovery and pharmacology on a global scale.
Keur Massar Traditional Hospital. One dataset used was provided by H^opital Traditionnel de Keur Massar2 in Senegal founded in 1980 by Professor Yvette Pares3, who had years of experience working with traditional practitioners from di erent ethnic groups in Senegal. This hospital has a strong focus on phytotherapy along ATM traditions and produces medicinal plant preparations (about 1,000 products and recipes) from their botanical garden.
Datasets. Besides this dataset, two datasets on ATM practice in Madagascar
were used. The rst contains data on medicinal plants used to treat the six
most frequent diseases in the Ambalabe rural community in Madagascar [
We then linked the plant- and disease data in our datasets to resources on BioPortal4 and DBpedia5. The BioPortal REST API was used to search for URIs for terms across ontologies, such as the Human Disease Ontology 6, the
3 Head of the department of Plant Biology at Cheikh Anta Diop University in Dakar
Symptom Ontology 7 and SNOMEDCT 8. If no URI is found, a synonym of the term is entered or linked to a proper resource on DBpedia.
Dataset conversion and linking. Our three datasets (.CSV) were translated into RDF and linked to the target datasets. First, columns from the original tabular data are stored in a dictionary as values, and assigned to a new key object name: 'set2 99': [u'Emilia humifusa DC. Rakotoarivelo', u'Asteraceae', u'Infected wound', u'L', u'Angea', u'Cataplasm on wound', u'400', 0.02, u'ID', 25L] A new data model was created that does not use existing ontologies, with converted triples of the form atmData:set2 99 atmVocab:familyName dbpg:Asteraceae.
Results. The result of this conversion is a total of 13,028 triples, of 672 plant types that treat 1,799 health conditions. The data is available at a public GIT repository at https://github.com/biktorrr/linkedatmdata. For live browsing and querying, we provide a triple store endpoint at http://semanticweb. cs.vu.nl/linkedatm/home. The 672 set objects all have the same structure as the below RDF Turtle snippet shows. atmData : s e t 2 9 9 atmVocab : ailment atmData : Infected wound ; atmVocab : binomialName atmData : Emilia humifusa DC. R a k o t o a r i v e l o ; atmVocab : familyName dbpg : Asteraceae ; atmVocab : p l a n t P a r t s "L"^^ xsd : s t r i n g ; atmVocab : p r e p a r a t i o n A d m i n i s t r a t i o n " Cataplasm on wound"@en .
New and existing pre xes are used to simplify SPARQL queries with: p r e f i x dbr : <http : / / dbpedia . org / r e s o u r c e/> p r e f i x dbo : <http : / / dbpedia . org / ontology/> p r e f i x atmData : <http : / / semanticweb . cs . vu . n l / linkedatm / r e s o u r c e > p r e f i x atmVocab : <http : / / semanticweb . cs . vu . n l / linkedatm / vocab/> p r e f i x p r l : <http : / / p u r l . o b o l i b r a r y . org /obo/> p r e f i x snmd : <http : / / p u r l . b i o o n t o l o g y . org / ontology /SNOMEDCT/>
By querying possible outcomes of the use cases, the proof of concept is validated. Consider the following use case: How does Malaria treatment di ers in two di erent regions in Madagascar? SELECT ? ailment ? f a m i l y ? binomialName ? p a r t s ? p r e p a r a t i o n WHERE f ? s atmVocab : familyName ? f a m i l y . ? s atmVocab : binomialName ? binomialName . ? binomialName r d f s : l a b e l ? binomial . ? s atmVocab : genusType ? genusType . ? s atmVocab : ailment j atmVocab : d i s e a s e T r e a t e d ? ailment . ? ailment r d f s : l a b e l ? ailmentLabel . ? s atmVocab : p l a n t P a r t s ? p a r t s . ? s atmVocab : p r e p a r a t i o n j atmVocab : p r e p a r a t i o n A d m i n i s t r a t i o n ? p r e p a r a t i o n .
FILTER(? ailmentLabel = " Malaria "@en) g GROUP BY ? ailment ? f a m i l y ? binomialName ? p a r t s ? p r e p a r a t i o n LIMIT 6
As Fig. 1 shows, di erences in plant use and treatment of malaria occur even between neighboring regions. While the Ambalabe community uses several parts, the other regions predominantly use the leaves. Thus, LinkedATM could both contribute to collaborative knowledge sharing between communities, and preserve critical knowledge for future generations.
The second case focuses on the di erence in use of a speci c plant species between Senegal and Madagascar.The Sclerocarya birrea tree grows in both Southern and West Africa. However, the treatment purposes di er per country. The following query shows its use in Senegal: The family of the tree (i.e. Anacardiaceae) is unknown and is identi ed by linking the known genus type to DBpedia. Fig. 2 shows that the tree is used to treat hypoglycemia, as an anti-infective, an antivenin or an astringent for the skin.
The aforementioned communities in Southern Madagascar on the other hand, use this tree to treat malaria, in prenatal care, postpartum recovery, dizziness during pregnancy, fever and caries, as shown in Fig. 3. This paper stipulates that formalizing ATM, and linking it to conventional medicine, can yield innovative knowledge bene ting both ATM practitioners and researchers on the usage of plant components in conventional pharmacology. Our proof of concept focuses on two concrete use cases, using data from an ATM hospital in Senegal and the results from earlier use cases in Madagascar. This has resulted in a dataset of 13,028 RDF triples, which describe 672 plant types and 1,799 health conditions. The data has been linked to knowledge on the Web (BioPortal & DBpedia), and shown to be of interest in the use cases. Further steps are required, including obtaining more comprehensive and varied data, linked to pharmaceutical ontologies for comparison with conventional medicinal substances. Making the data accessible to ATM practitioners in rural areas in Africa would require the development of an o ine and voice-based version. Semantic Web techniques can, without a doubt, contribute to Linked ATM. To achieve this, there are challenges to overcome. However, as was demonstrated in this proof on concept paper, the rst step has been taken.
Acknowledgements. We thank Djibril B^a and Genevieve Baumann for their input. This work is supported by the W4RA initiative (http://w4ra.org).