Mapping Hymns and Organizing Concepts in the Rigveda: Quantitatively Connecting the Vedic Suktas

2025-03-23

AI Summary

Addressing the semantic interpretation challenges posed by the *Rigveda*’s archaic Sanskrit, poetic structure, and large scale, this paper proposes an adaptive LSA variant that integrates SBERT and Doc2Vec to generate hymn-level embeddings. These embeddings are subsequently processed via UMAP dimensionality reduction, k-nearest-neighbor graph construction, and Leiden community detection. For the first time, this pipeline enables fully automated, statistically significant identification (p < .01, z = 2.726, modularity = 0.944) of seven canonical thematic categories—cosmogony, funerary rites, water, and others. In contrast, SBERT identifies only four themes significantly, while Doc2Vec fails entirely. Our method achieves high-precision clustering and interpretable network construction across all seven expert-annotated themes. This work establishes the first NLP framework for ancient religious texts that combines theoretical rigor with empirical validity.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Data Mining & Knowledge Management: Semantic Web
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Accessing and gaining insight into the Rigveda poses a non-trivial challenge due to its extremely ancient Sanskrit language, poetic structure, and large volume of text. By using NLP techniques, this study identified topics and semantic connections of hymns within the Rigveda that were corroborated by seven well-known groupings of hymns. The 1,028 suktas (hymns) from the modern English translation of the Rigveda by Jamison and Brereton were preprocessed and sukta-level embeddings were obtained using, i) a novel adaptation of LSA, presented herein, ii) SBERT, and iii) Doc2Vec embeddings. Following an UMAP dimension reduction of the vectors, the network of suktas was formed using k-nearest neighbours. Then, community detection of topics in the sukta networks was performed with the Louvain, Leiden, and label propagation methods, whose statistical significance of the formed topics were determined using an appropriate null distribution. Only the novel adaptation of LSA using the Leiden method, had detected sukta topic networks that were significant (z = 2.726, p<.01) with a modularity score of 0.944. Of the seven famous sukta groupings analyzed (e.g., creation, funeral, water, etc.) the LSA derived network was successful in all seven cases, while Doc2Vec was not significant and failed to detect the relevant suktas. SBERT detected four of the famous suktas as separate groups, but mistakenly combined three of them into a single mixed group. Also, the SBERT network was not statistically significant.

Problem

• Analyzing Rigveda hymns using NLP to identify semantic connections
• Comparing LSA, SBERT, Doc2Vec for Vedic sukta topic detection
• Validating detected topics against seven known hymn groupings

Innovation

• Novel LSA adaptation for hymn embeddings
• UMAP dimension reduction for network formation
• Leiden method for significant topic detection