@inproceedings{kumarasamy-etal-2026-thiruppugazh,
title = "Thiruppugazh-{KG} Dataset: A Manually Annotated Resource for Computational Analysis of {T}amil Devotional Literature",
author = "Kumarasamy, Garthigan and
Thevakumar, Jubeerathan and
Uthayakumar, Sathurgini and
Kajanath, Disne and
Sivalingam, Narthana and
Thayasivam, Uthayasanker",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.8/",
pages = "62--70",
ISBN = "979-8-89176-401-9",
abstract = "This paper introduces Thiruppugazh-KG, a semantically annotated dataset and knowledge graph derived from the Thiruppugazh corpus, a 14th-century collection of 1,335 Tamil devotional hymns composed by Arunagirinathar. The dataset includes annotations for entities, devotional themes, mythological events, philosophical concepts, imagery, and sacred locations mentioned in each hymn. Using these annotations, we construct a Neo4j-based knowledge graph that models relationships between hymns and their associated cultural and narrative elements. Graph analytics, including PageRank, are applied to identify prominent entities and sacred locations within the corpus. The resulting resource provides a structured representation of Tamil devotional literature and supports computational analysis of cultural texts in low-resource languages."
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%0 Conference Proceedings
%T Thiruppugazh-KG Dataset: A Manually Annotated Resource for Computational Analysis of Tamil Devotional Literature
%A Kumarasamy, Garthigan
%A Thevakumar, Jubeerathan
%A Uthayakumar, Sathurgini
%A Kajanath, Disne
%A Sivalingam, Narthana
%A Thayasivam, Uthayasanker
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F kumarasamy-etal-2026-thiruppugazh
%X This paper introduces Thiruppugazh-KG, a semantically annotated dataset and knowledge graph derived from the Thiruppugazh corpus, a 14th-century collection of 1,335 Tamil devotional hymns composed by Arunagirinathar. The dataset includes annotations for entities, devotional themes, mythological events, philosophical concepts, imagery, and sacred locations mentioned in each hymn. Using these annotations, we construct a Neo4j-based knowledge graph that models relationships between hymns and their associated cultural and narrative elements. Graph analytics, including PageRank, are applied to identify prominent entities and sacred locations within the corpus. The resulting resource provides a structured representation of Tamil devotional literature and supports computational analysis of cultural texts in low-resource languages.
%U https://aclanthology.org/2026.dravidianlangtech-1.8/
%P 62-70
Markdown (Informal)
[Thiruppugazh-KG Dataset: A Manually Annotated Resource for Computational Analysis of Tamil Devotional Literature](https://aclanthology.org/2026.dravidianlangtech-1.8/) (Kumarasamy et al., DravidianLangTech 2026)
ACL