待翻譯：NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts

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--> [Submitted on 8 Jul 2026] Title:NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts View a PDF of the paper titled NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts, by M. S. Rajeevan and 2 other authors View PDF Abstract:Ancient Indian medical texts like Sushruta Samhita have extensive information on diseases, treatments, and surgical techniques. Yet, their ancient format and use of intricate vocabulary pose difficulties in accessibility and systematic ordering. The research here utilizes Natural Language Processing (NLP) methods like Named Entity Recognition (NER), BERTopic modeling, and Knowledge Graph development in Neo4j to extract, categorize, and visualize important concepts based on translated versions. Thematic classification with BERTopic allows for the identification of the underlying medical topics, whereas NER supports the structured entity recognition of diseases, treatments, researchers, and medicinal plants. Graphbased network analysis with Neo4j also allows for the semantic representation of relationship among extracted entities, supporting knowledge retrieval and digital preservation. The findings illustrate how graph databases, topic modeling, and entity recognition facilitate the computational organization of Ayurveda's historical medical wisdom, closing the gap between the conventional texts and contemporary data-driven inquiry. The suggested method promotes historical text analysis, medical informatics, and digital humanities to make ancient Indian medical wisdom more accessible and understandable. Comments: 19 pages, 8 figures, 5 tables. Presented at the National Conference on "Reimagining LIS Education: Integrating Indian Knowledge Systems with NEP 2020" (March 2025), organized by Tata Institute of Social Sciences (TISS) and the Indian Association of Teachers of Library and Information Science (IATLIS). Recipient of the Best Paper Award Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) ACM classes: I.2.7; H.3.3 Cite as: arXiv:2608.28608 [cs.CL] (or arXiv:2608.28608v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.28608 arXiv-issued DOI via DataCite Journal reference: In Proc. TISS-IATLIS National Conference 2025: Reimagining LIS Education: Integrating Indian Knowledge Systems with NEP 2020, Vol. 1, p. 351, 2025 Submission history From: Rajeevan M S [view email] [v1] Wed, 8 Jul 2026 09:46:15 UTC (1,082 KB) Full-text links: Access Paper: View a PDF of the paper titled NLP-Driven Knowledge Extraction and Thematic Classification of Translated Ancient Indian Medical Texts, by M. S. Rajeevan and 2 other authors View PDF view license Current browse context: cs.CL new | recent | 2026-08 Change to browse by: cs cs.AI cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)