NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts
The paper applies NLP techniques to structured extraction and classification of knowledge from translated ancient Indian medical texts, specifically the Sushruta Samhita and Ayurvedic literature. A three-pronged pipeline is used: Named Entity Recognition (NER) for extracting diseases, treatments, researchers, and medicinal plants; BERTopic modeling for thematic classification of medical concepts; and Neo4j knowledge graph construction for semantic relationship visualization. The approach bridges
Analysis
TL;DR
- The paper applies NLP techniques to structured extraction and classification of knowledge from translated ancient Indian medical texts, specifically the Sushruta Samhita and Ayurvedic literature.
- A three-pronged pipeline is used: Named Entity Recognition (NER) for extracting diseases, treatments, researchers, and medicinal plants; BERTopic modeling for thematic classification of medical concepts; and Neo4j knowledge graph construction for semantic relationship visualization.
- The approach bridges traditional textual scholarship and modern data-driven inquiry, enabling computational organization of historical medical wisdom.
- The methodology is positioned at the intersection of digital humanities, medical informatics, and historical text analysis, with implications for making ancient knowledge systems accessible to contemporary research.
- The work was presented at the TISS-IATLIS National Conference 2025 and submitted to arXiv in July 2026 under cs.CL.
Why It Matters
This research demonstrates a practical pipeline for applying modern NLP and knowledge graph techniques to culturally and historically significant texts that have long been inaccessible to systematic computational analysis. For AI practitioners, it showcases how combining NER, topic modeling, and graph databases can unlock structured insights from unstructured, domain-specific corpora — a pattern directly transferable to other historical, legal, or specialized text domains.
Technical Details
- Named Entity Recognition (NER): Used to identify and categorize key entities including diseases, treatments, researchers, and medicinal plants from translated Ayurvedic texts, enabling structured data extraction from unstructured prose.
- BERTopic Modeling: Applied for thematic classification, identifying underlying medical topics and clustering related concepts across the corpus to reveal the semantic structure of ancient medical knowledge.
- Neo4j Knowledge Graph: Extracted entities and their relationships were modeled as a graph database, enabling semantic network analysis, relationship traversal, and visual exploration of connections between medical concepts.
- Pipeline Integration: The three components form a cohesive workflow — NER extracts entities, BERTopic classifies themes, and Neo4j visualizes relationships — providing both categorical and relational understanding of the source texts.
- Domain: The work falls under Computation and Language (cs.CL), Artificial Intelligence (cs.AI), and Information Retrieval (cs.IR), with ACM classifications I.2.7 and H.3.3.
Industry Insight
- The pipeline demonstrated here is highly generalizable: any domain with rich but poorly structured historical or specialized texts (legal archives, religious scriptures, scientific manuscripts) can benefit from the same NER + topic modeling + knowledge graph approach.
- As institutions increasingly prioritize digital preservation of cultural heritage, this work provides a replicable blueprint for converting analog or legacy textual collections into queryable, interconnected knowledge bases.
- The integration of graph databases with LLM-adjacent techniques like BERTopic signals a growing trend toward hybrid AI systems that combine symbolic knowledge representation with neural semantic understanding — a direction practitioners should monitor for building robust, interpretable AI applications.
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