The Knowledge Engine – Crowdsourcing and Computational Ontologies for Ayurveda
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Convenors: Dr. Ishaan Gupta, Dr. Rakesh Narayanan and Dr. Asif Iqbal
Team members – Dr. Deeksha Varshney, Dr. Divya Saxena, Dr. Saketh Ram Thrigulla, Prof. Mitali Mukerji
Volunteers – Aditi Joshi, Dr. Deepika Jangir
Key Pointers:
How can Natural Language Processing (NLP) bridge the gap between ancient Sanskrit-based texts and modern, interoperable digital health platforms?
How do Knowledge Graphs map foundational Ayurvedic concepts to modern medical terminology to ensure Generative AI provides accurate, cross-disciplinary insights?
What computational tools are most effective for validating Ayurvedic principles against contemporary system-level biological modeling?
This session focuses on the computational pipelines required to digitize and scale traditional Ayurvedic wisdom. By utilizing AI and Natural Language Processing (NLP), we aim to bridge the gap between ancient texts and modern interoperable digital platforms, creating a unified data resource for the PRISM framework.
Key Discussion Points:
Crowdsourcing for Digitalized Knowledge: Implementing advanced NLP techniques to extract, categorize, and digitize vast amounts of Ayurvedic knowledge from classical texts and modern medicine.
Knowledge Graphs for GenAI Interoperability: Developing structured knowledge graphs that map foundational Ayurvedic concepts to modern medical terminology, enabling Generative AI to provide accurate, cross-disciplinary health insights.
Computational Tools for Biological Validation: Utilizing bioinformatics and system-level modeling to provide the theoretical "concordance" needed to validate Ayurvedic principles against contemporary biological findings.
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