Abstract
This paper presents a comparative analysis of the various formal models that can be used to represent a story. The analysis focuses on two types of representation families: semantics-based representations, which use ontologies, and process-based representations. The aim is to provide a comparative overview of the models, analyzing their weaknesses and strengths, in order to determine the formal model that best lends itself to modeling a story by highlighting its main components in terms of the actors involved, events, actions, spatio-temporal relations, as well as cause and effect, in hopes of identifying the formal story representation model that can be used as the starting point for developing a framework that can perform automated storytelling generation. Finally, examples are given of the uses of these models to represent a mythological story.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antunes, P., Simões, D., Carriço, L., Pino, J.A.: An end-user approach to business process modeling. J. Netw. Comput. Appl. 36(6), 1466–1479 (2013)
Concepción, E., Gervás, P., Méndez, G.: A common model for representing stories in automatic storytelling. In: 6th International Workshop on Computational Creativity, Concept Invention, and General Intelligence, C3GI 2017, Madrid, Spain (2017)
Di Martino, B., Cante, L.C., Esposito, A., Graziano, M.: A tool for the semantic annotation, validation and optimization of business process models. Softw.: Pract. Experience (2023)
Di Martino, B., Esposito, A., Cante, L.C.: Multi agents simulation of justice trials to support control management and reduction of civil trials duration. J. Ambient Intell. Hum. Comput. 1–13 (2021)
Di Martino, B., Graziano, M., Colucci Cante, L., Esposito, A., Epifania, M.: Application of business process semantic annotation techniques to perform pattern recognition activities applied to the generalized civic access. In: Barolli, L. (ed.) CISIS 2022. LNNS, vol. 497, pp. 404–413. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08812-4_39
Guerra-Hernández, A., El Fallah-Seghrouchni, A., Soldano, H.: Learning in BDI multi-agent systems. In: Dix, J., Leite, J. (eds.) CLIMA 2004. LNCS (LNAI), vol. 3259, pp. 218–233. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30200-1_12
Landkammer, M., Hinkelmanns, P., Schwembacher, M., Zeppezauer-Wachauer, K., Nicka, I.: ONAMA. Ontology of Narratives of the Middle Ages: Ontology 1.5. University of Salzburg. Salzburg (2020). https://doi.org/10.5281/zenodo.4285987. Accessed 23 Nov 2020
Liu, S., Yingcai, W., Wei, E., Liu, M., Liu, Y.: StoryFlow: tracking the evolution of stories. IEEE Trans. Vis. Comput. Graph. 19(12), 2436–2445 (2013)
Lutterbach, B.: Theseus and the minotaur (2016)
Mann, W.C., Thompson, S.A.: Rhetorical structure theory: a theory of text organization. University of Southern California, Information Sciences Institute Los Angeles (1987)
Nakasone, A., Ishizuka, M.: Storytelling ontology model using RST. In: 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 163–169 (2006)
Natschläger, C.: Towards a BPMN 2.0 ontology. In: Dijkman, R., Hofstetter, J., Koehler, J. (eds.) BPMN 2011. LNBIP, vol. 95, pp. 1–15. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25160-3_1
Simões, D., Antunes, P., Cranefield, J.: Enriching knowledge in business process modelling: a storytelling approach. In: Razmerita, L., Phillips-Wren, G., Jain, L.C. (eds.) Innovations in Knowledge Management. ISRL, vol. 95, pp. 241–267. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-47827-1_10
Valls-Vargas, J., Zhu, J., Ontañón, S.: Towards automatically extracting story graphs from natural language stories. In: AAAI Workshops (2017)
The work described in this paper has been supported by the research project RASTA: Realtà Aumentata e Story-Telling Automatizzato per la valorizzazione di Beni Culturali ed Itinerari; Italian MUR PON Proj. ARS01 00540.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Colucci Cante, L., Di Martino, B., Graziano, M. (2023). A Comparative Analysis of Formal Storytelling Representation Models. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-031-35734-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-35734-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35733-6
Online ISBN: 978-3-031-35734-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)