TY - GEN
T1 - Knowledge-based Service for African Traditional Herbal Medicine
T2 - 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019
AU - Devine, Samuel Nii Odoi
AU - Kolog, Emmanuel Awuni
AU - Sutinen, Erkki
AU - Sääksjärvi, Ilari
N1 - Publisher Copyright:
Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
PY - 2019
Y1 - 2019
N2 - Globally, the acceptance and use of herbal and traditional medicine is on the rise. Africa, especially Ghana, has its populace resorting to African Traditional Herbal Medicine (ATHMed) for their healthcare needs due to its potency and accessibility. However, the practice involving its preparation and administration has come into question. Even more daunting is the poor and inadequate documentation covering the preservation and retrieval of knowledge on ATHMed for long-term use, resulting in invaluable healthcare knowledge being lost. Consequently, there is the need to adopt strategies to help curtail the loss of such healthcare knowledge, for the benefit of ATHMed stakeholders in healthcare delivery, industry and academia. This paper proposes a hybrid-based computational knowledge framework for the preservation and retrieval of traditional herbal medicine. By the hybrid approach, the framework proposes the use of machine learning and ontology-based techniques. While reviewing literature to reflect the existing challenges, this paper discusses current technologies suited to approach them. This results in a framework that embodies an ontology driven knowledge-based system operating on a semantically annotated corpus that delivers a contextual search pattern, geared towards a formalized, explicit preservation and retrieval mechanism for safeguarding ATHMed knowledge.
AB - Globally, the acceptance and use of herbal and traditional medicine is on the rise. Africa, especially Ghana, has its populace resorting to African Traditional Herbal Medicine (ATHMed) for their healthcare needs due to its potency and accessibility. However, the practice involving its preparation and administration has come into question. Even more daunting is the poor and inadequate documentation covering the preservation and retrieval of knowledge on ATHMed for long-term use, resulting in invaluable healthcare knowledge being lost. Consequently, there is the need to adopt strategies to help curtail the loss of such healthcare knowledge, for the benefit of ATHMed stakeholders in healthcare delivery, industry and academia. This paper proposes a hybrid-based computational knowledge framework for the preservation and retrieval of traditional herbal medicine. By the hybrid approach, the framework proposes the use of machine learning and ontology-based techniques. While reviewing literature to reflect the existing challenges, this paper discusses current technologies suited to approach them. This results in a framework that embodies an ontology driven knowledge-based system operating on a semantically annotated corpus that delivers a contextual search pattern, geared towards a formalized, explicit preservation and retrieval mechanism for safeguarding ATHMed knowledge.
KW - African Traditional Herbal Medicine
KW - Information Retrieval
KW - Knowledge-base
KW - Machine Learning
KW - Ontology
UR - http://www.scopus.com/inward/record.url?scp=85074170686&partnerID=8YFLogxK
U2 - 10.5220/0007946400450055
DO - 10.5220/0007946400450055
M3 - Conference contribution
AN - SCOPUS:85074170686
T3 - IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
SP - 45
EP - 55
BT - IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
A2 - Bernardino, Jorge
A2 - Salgado, Ana
A2 - Filipe, Joaquim
PB - SciTePress
Y2 - 17 September 2019 through 19 September 2019
ER -