Rule-Based Myanmar Herbal Recommendation System Using Ontology

Abstract: Myanmar herbal medicine is recognized as a vital component of traditional healthcare; however, its documentation remains disorganized and primarily available in the local language. Identifying appropriate herbs for individual users from existing records is inefficient and may result in medication errors. This study presents a formalized, digitized representation of Myanmar herbal knowledge using an ontology-based framework that enables precise and efficient herb identification and recommendation. The ontology and rule-based recommendation system were developed through literature review, expert consultation, and analysis of volumes 1 and 2 of Medicinal Plants of Myanmar. The system’s performance was evaluated by three experts from the University of Traditional Medicine in Mandalay. The constructed ontology models 119 herbs, 17 plant parts, 12 distribution regions, 256 disease symptoms, and 23 adverse effects. Seven inference rules were defined to generate recommendations based on seven benchmark questions. The system achieved an average accuracy of 95% and a recall of 96% in recommending herbs based on symptoms, plant parts used, location, plant family, adverse effects, combinations of users’ symptoms and location, and combinations of symptoms and adverse effects through rule-based evaluations. The proposed system provides a formalized structure for preserving Myanmar herbal knowledge and offers reliable recommendations within the scope of a limited dataset and a rigid ontology structure.

Nang Saing Horm and Nikom Suvonvorn. “Rule-Based Myanmar Herbal Recommendation System Using Ontology”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170324

@article{Horm2026,
title = {Rule-Based Myanmar Herbal Recommendation System Using Ontology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170324},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170324},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Nang Saing Horm and Nikom Suvonvorn}
}