Toward a Formal Ontology for Narrative

Ciotti, Fabio

doi:10.14195/2182-8830_4-1_2

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Following the suggestion expressed by Elam, our goal, here, is to formalize a theory of dramatic action (Elam, 1980), which may subsequently accommodate in its framework an actantial structure drawn from semiotic studies. A relevant contribution in this sense is provided by the narrative ontology proposed by Ciotti (2016): mainly based on the tradition of semiotic and structuralist narratology, it contains key notions such as Actant, Action, and Actor. The use of the computational ontology format not only allows the encoding of the conceptual model of drama in a formal, unambiguous way, as called for by Varela (2016), but also makes the knowledge about drama both available as a vocabulary for the interchange of annotations across different projects and readily usable as a representational tool for applications that process and manipulate these annotations in automatic ways.…”

Section: Background and Motivationsmentioning

confidence: 99%

The ontology of drama

Damiano

Lombardo

Pizzo

2019

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In this paper, we describe the ontology of drama called Drammar. Pervasive in old and new media, and relevant from a multi-disciplinary perspective that ranges from literary criticism and semiotics to aesthetics and psychology, the manifestations of drama are ubiquitous in today's culture, overcoming the boundaries of genres and formats. Drawing from an extensive survey of the literature on drama, Drammar formalizes the elements of drama in a media-and task-independent way. Drammar is encoded in OWL2 to provide an interoperable formal model for representing and studying drama in a variety of systems. Issued by a research initiative carried out in an interdisciplinary way over a decade, Drammar abstracts from the media and languages by which drama is conveyed, providing a formal systematization of the core notions about drama that leverages the theories and models of agency in the tradition of Artificial Intelligence. In this paper, we illustrate the ontology, describing its design and motivations through examples and use cases.

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Section: Background and Motivationsmentioning

confidence: 99%

The ontology of drama

Damiano

Lombardo

Pizzo

2019

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“…literary themes) in each domain and their heights from the root LTO class. LTO is engineered to fit within the Basic Formal Ontology (BFO) top level ontology class hierarchy [48] in order to facilitate interoperability with other emerging fiction studies ontologies [49][50][51][52][53]. LTO is meant to cover important, operationally verifiable literary themes that can be expected reoccur in multiple works of fiction [7].…”

Section: The Star Trek Television Seriesmentioning

confidence: 99%

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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Collaborative filtering based recommender systems have been extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain when items mostly belong to the same category and are mostly alike (e.g. television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN algorithm. As study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We observed that our proposed method showed to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

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