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A Classification Benchmark Based on the Literary Theme Ontology Cover

A Classification Benchmark Based on the Literary Theme Ontology

Open Access
|Feb 2026

References

  1. Almeida, P. D., & Gnoli, C. (2021). Fiction in a phenomenon-based classification. Cataloging & Classification Quarterly, 59(5), 477491. 10.1080/01639374.2021.1946232
  2. Armstrong, R., & Armstrong, M. (2001). Encyclopedia of film themes, settings and series. McFarland.
  3. Baker, S. L., & Shepherd, G. W. (1987). Fiction classification schemes: the principles behind them and their success. RQ, 245251.
  4. Bamman, D., Chang, K. K., Lucy, L., & Zhou, N. (2024). On classification with large language models in cultural analytics. In Proceedings of Computational Humanities Research. Retrieved from https://ceur-ws.org/Vol-3834/paper119.pdf
  5. Bartalesi, V., & Meghini, C. (2017). Using an ontology for representing the knowledge on literary texts: The Dante Alighieri case study. Semantic Web, 8(3), 385394. 10.3233/SW-150198
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 9931022.
  7. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135146. 10.1162/tacl_a_00051
  8. Bremond, C., Landy, J., & Pavel, T. (Eds.) (1995). Thematics: New approaches. SUNY Press.
  9. Brinker, M. (1993). Theme and interpretation (in sollors, werner ed. the return of thematic criticism cambridge, massachusetts. Harvard University Press.
  10. Encyclopedia of Science Fiction contributors. (2025). The Encyclopedia of Science Fiction. Retrieved from http://www.sf-encyclopedia.com (Accessed: 12-Nov-2025).
  11. Fandom. (2025). Fandom. Retrieved from https://www.fandom.com (Accessed: 14-Nov-2025).
  12. Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., … Ma, Z. (2024). The llama 3 herd of models. Retrieved from https://arxiv.org/abs/2407.21783
  13. Hagedorn, J., & Darányi, S. (2022). Bearing a bag-of-tales: An open corpus of annotated folktales for reproducible research. Journal of Open Humanities Data, 8(16). 10.5334/johd.78
  14. Hudson, W. H. (1913). An introduction to the study of literature. George G. Harrap & Company.
  15. Kamath, A., et al. (2025). Gemma 3: A multimodal addition to the gemma family of lightweight open models. arXiv preprint arXiv:2503.19786. 10.48550/arXiv.2503.19786
  16. Karsdorp, F., & van den Bosch, A. (2013). Identifying motifs in folktales using topic models. In Proceedings of the 22 annual belgian-dutch conference on machine learning (pp. 4149).
  17. Khan, F., Arrigoni, S., Boschetti, F., & Frontini, F. (2016). Restructuring a taxonomy of literary themes and motifs for more efficient querying. MATLIT: Materialities of Literature, 4(2), 1127. 10.14195/2182-8830_4-2_1
  18. Louwerse, M. M., & Van Peer, W. (2008). Thematics: interdisciplinary studies. John Benjamins Publishing Company.
  19. Lucy, L., Griffiths, C., Levine, S., Eberhardt, J. L., Demszky, D., & Bamman, D. (2025). Tell, don’t show: Leveraging language models’ abstractive retellings to model literary themes. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Findings of the Association for Computational Linguistics: ACL 2025 (pp. 2258522610). Vienna, Austria: Association for Computational Linguistics. Retrieved from https://aclanthology.org/2025.findings-acl.1162/
  20. Mark Pejtersen, A., & Austin, J. (1983). Fiction retrieval: Experimental design and evaluation of a search system based on users’ value criteria (part 1). Journal of Documentation, 39(4), 230246. 10.1108/eb026750
  21. Matveeva, M., & Malykh, V. (2022). Development of folklore motif classifier using limited data. In Conference on Artificial Intelligence and Natural Language (pp. 4048). 10.1007/978-3-031-23372-2_4
  22. McClinton-Temple, J. (2010). Encyclopedia of themes in literature (No. v. 1). Facts On File.
  23. Mistral AI. (2023). Mistral 7B. https://mistral.ai/news/announcing-mistral-7b/. (Accessed: 2025-07-17).
  24. Nguyen, D., Trieschnigg, D., & Theune, M. (2013). Folktale classification using learning to rank. In European Conference on Information Retrieval (pp. 195206). 10.1007/978-3-642-36973-5_17
  25. Onsjö, M., & Sheridan, P. (2020). Theme enrichment analysis: A statistical test for identifying significantly enriched themes in a list of stories with an application to the Star Trek television franchise. Digital Studies/Le champ numérique, 10(1), 1. 10.16995/dscn.316
  26. Onsjö, M., & Sheridan, P. (2025a). Literary Theme Ontology. GitHub release. Retrieved from https://github.com/theme-ontology/theming/releases/tag/v2025.04 (Accessed: 7 Nov. 2025).
  27. Onsjö, M., & Sheridan, P. (2025b). totolo – A Python package for working with data from the Theme Ontology theming repository. https://pypi.org/project/totolo/. (Version 2.1.2).
  28. Onsjö, M., & Sheridan, P. (2025c). Welcome to the Literary Theme Ontology tutorial. Retrieved from https://github.com/theme-ontology/theming/wiki (Accessed: 12 Nov. 2025).
  29. Open Subtitles. (2025). Open Subtitles. Retrieved from https://www.opensubtitles.org (Accessed: 14-Nov-2025).
  30. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(85), 28252830. Retrieved from http://jmlr.org/papers/v12/pedregosa11a.html
  31. Propp, V. (1968). Morphology of the folktale (2nd ed.; L. Scott, Trans.). Austin: University of Texas Press.
  32. Rimmon-Kenan, S. (1995). What is the theme and how do we get at it? In C. Bremond, J. Landy, & T. Pavel (Eds.), Thematics: New approaches (pp. 919). SUNY Press.
  33. Saarti, J. (2019). Fictional literature, classification and indexing. Knowledge Organization, 46(4), 320332. 10.5771/0943-7444-2019-4-320
  34. Seigneuret, J. (1988). Dictionary of literary themes and motifs (No. v. 1). Greenwood Press.
  35. Sheridan, P., Onsjö, M., & Hastings, J. (2019). The Literary Theme Ontology for media annotation and information retrieval. In A. Barton, S. Seppälä, & D. Porello (Eds.), Proceedings of the WODHSA. first International Workshop on Ontologies for Digital Humanities and Their Social Analysis. Part of the Fifth Joint Ontology Workshops (JOWO 2019) Episode V: The Styrian Autumn of Ontology. Joint Ontology Workshops. Retrieved from https://ceur-ws.org/Vol-2518/paper-WODHSA8.pdf
  36. Sollors, W. (1993). The return of thematic criticism (No. 18). Harvard University Press.
  37. Tunstall, L., Reimers, N., Jo, U. E. S., Bates, L., Korat, D., Wasserblat, M., & Pereg, O. (2022). Efficient few-shot learning without prompts. arXiv preprint arXiv:2209.11055.
  38. TV Trope contributors. (2025). TV Tropes — the all devouring pop-culture wiki. Retrieved from https://tvtropes.org (Accessed: 12-Nov-2025).
  39. TVSubs.net. (2025). TVSubs.net — TV Show Subtitles. Retrieved from https://www.tvsubs.net (Accessed: 14-Nov-2025).
  40. Uther, H.-J. (2004). The types of international folktales: A classification and bibliography. FF communications.
  41. Wikipedia contributors. (2025). List of science fiction themes — Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/List_of_science_fiction_themes (Accessed: 12-Nov-2025).
  42. Yarlott, W. V. H., & Finlayson, M. A. (2016). Learning a better motif index: Toward automated motif extraction. In 7th Workshop on Computational Models of Narrative (CMN 2016) (pp. 71).
DOI: https://doi.org/10.5334/johd.480 | Journal eISSN: 2059-481X
Language: English
Submitted on: Nov 14, 2025
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Accepted on: Jan 19, 2026
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Published on: Feb 18, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Noa Visser Solissa, Paul Sheridan, Mikael Onsjö, Andreas van Cranenburgh, Federico Pianzola, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.