Story Traces and Projections: Exploring the Patterns of Storytelling

This paper describes a framework for building story traces (compact global views of a narrative) and story projections (selections of key story elements) and their applications in digital storytelling. Word and sense properties are extracted using the WordNet lexical database enhanced with Prolog inference rules and a number of lexical transformations. Inference rules are based on navigation in various WordNet relation chains (hypernyms, meronyms, entailment and causality links, etc.) and derived inferential closures expressed as boolean combinations of node and edge properties used to direct the navigation. The resulting abstract story traces provide a compact view of the underlying story’s key content elements and a means for automated indexing and classification of story collections [1, 2]. Ontology driven projections act as a kind of “semantic lenses” and provide a means to select a subset of a story whose key sense elements are subsumed by a set of concepts, predicates and properties expressing the focus of interest of a user. Finally, we discuss applications of these techniques in story understanding, classification of digital story collections, story generation and story-related question answering. The main contribution of the paper consists in the use of a lexical knowledge base together with an advanced rule based inference mechanism for understanding stories, and the use of the information extracted by this process for various applications.