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

Collaborative filtering based recommender systems have proven to be 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 for items that mostly belong to the same category (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 (K-nearest neighbors) algorithm. As a 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 found our proposed method 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.Comment: 25 pages, 6 figures, 5 tables, minor revision

Text in the Natural World: Topics of Evolutionary Theory of Literature

The study of literature has expanded to include an evolutionary perspective. Its premise is that the literary text and literature as an overarching institution came into existence as a product of the same evolutionary process that gave rise to the human species. In this view, literature is an evolutionary adaptation that functions as any other adaptation does, as a means of enhancing survivability and also promoting benefits for the individual and society. Text in the Natural World is an introduction to the theory and a survey of topics pertinent to the evolutionary view of literature. After a polemical, prefatory chapter and an overview of the pertinent aspects of evolutionary theory itself, the book examines integral building blocks of literature and literary expression as effects of evolutionary development. This includes chapters on moral sense, symbolic thought, literary aesthetics in general, literary ontology, the broad topic of form, function and device in literature, a last theoretical chapter on narrative, and a chapter on literary themes. The concluding chapter builds on the preceding one as an illustration of evolutionary thematic study in practice, in a study of the fauna in the fiction of Maupassant. This text is designed to be of interest to those who read and think about things literary, as well as to those who have interest in the extension of Darwin’s great idea across the horizon of human culture. It tries to bridge the gulf that has separated the humanities from the sciences, and would be a helpful text for courses taught in both literary theory and interdisciplinary approaches to literature and philosophy.https://cupola.gettysburg.edu/books/1125/thumbnail.jp

LORE: A Compound Object Authoring and Publishing Tool for Literary Scholars based on the FRBR

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-06-04 10:30 AM – 12:00 PMThis paper presents LORE (Literature Object Re-use and Exchange), a light-weight tool designed to enable scholars and teachers of literature to author, edit and publish OAI-ORE-compliant compound information objects that encapsulate related digital resources and bibliographic records. LORE provides a graphical user interface for creating, labelling and visualizing typed relationships between individual objects using terms from a bibliographic ontology based on the IFLA FRBR. After creating a compound object, users can attach metadata and publish it to a Fedora repository (as an RDF graph) where it can be searched, retrieved, edited and re-used by others. LORE has been developed in the context of the Australian Literature Resource project (AustLit) and hence focuses on compound objects for teaching and research within the Australian literature studies community.NCRIS National eResearch Architecture Taskforce (NeAT

Computational Models (of Narrative) for Literary Studies

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1

Exploring manuscripts: sharing ancient wisdoms across the semantic web

Recent work in digital humanities has seen researchers in-creasingly producing online editions of texts and manuscripts, particularly in adoption of the TEI XML format for online publishing. The benefits of semantic web techniques are un-derexplored in such research, however, with a lack of sharing and communication of research information. The Sharing Ancient Wisdoms (SAWS) project applies linked data prac-tices to enhance and expand on what is possible with these digital text editions. Focussing on Greek and Arabic col-lections of ancient wise sayings, which are often related to each other, we use RDF to annotate and extract seman-tic information from the TEI documents as RDF triples. This allows researchers to explore the conceptual networks that arise from these interconnected sayings. The SAWS project advocates a semantic-web-based methodology, en-hancing rather than replacing current workflow processes, for digital humanities researchers to share their findings and collectively benefit from each other’s work

Implementing feedback in creative systems : a workshop approach

One particular challenge in AI is the computational modelling and simulation of creativity. Feedback and learning from experience are key aspects of the creative process. Here we investigate how we could implement feedback in creative systems using a social model. From the field of creative writing we borrow the concept of a Writers Workshop as a model for learning through feedback. The Writers Workshop encourages examination, discussion and debates of a piece of creative work using a prescribed format of activities. We propose a computational model of the Writers Workshop as a roadmap for incorporation of feedback in artificial creativity systems. We argue that the Writers Workshop setting describes the anatomy of the creative process. We support our claim with a case study that describes how to implement the Writers Workshop model in a computational creativity system. We present this work using patterns other people can follow to implement similar designs in their own systems. We conclude by discussing the broader relevance of this model to other aspects of AI