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Ayurveda, India’s classical medical tradition, encodes rich diagnostic and therapeutic frameworks in Sanskrit texts. However, due to their linguistic complexity and symbolic representations, these texts remain difficult to access in modern medical practice. This study presents a Natural Language Processing (NLP)-based framework for decoding Ayurvedic concepts, using Jwara (fever) from the Charaka Samhita as a representative case. A semantic mapping pipeline incorporating Indic NLP, mBERT, BioBERT, and biomedical ontologies (UMLS, ICD-10) was developed. Key outcomes included 85% accuracy in Sanskrit tokenization, 70% meaningful translation, and 60% alignment of Ayurvedic terms with biomedical concepts. Despite challenges in mapping metaphoric or unique concepts like Ama, the study demonstrates feasibility for bridging traditional and modern knowledge systems. The approach can support AI-driven tools for integrative healthcare and medical education.
Keywords:
Ayurveda, Jwara, Natural Language Processing, Biomedical Ontology, Sanskrit NLP, ICD-10
Cite Article:
"Decoding Ancient Wisdom: Leveraging Natural Language Processing for Semantic Mapping of Ayurvedic Texts – A Case Study on Jwara (Fever)", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a862-a864, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507093.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator