From Story to Narrative Network . A perspective based on network theory and data sciences
Résumé
Computational storytelling is an emerging discipline which gives room to many active researches. Beyond artistic and literary fields like trans-media storytelling, computational storytelling becomes a key technics used in Organizational storytelling, Learning and knowledge engineering to convey tacit knowledge. Computational storytelling starts with a claim of story being a part of knowledge representation paradigm. It allows the development of innovative systems like Case-based reasoning systems or more recently semantic web technology based on Natural language processing or statistical modeling techniques. But 'text-like' approaches of narratives are thereby limited to text excluding context. A second step deals with natural, oral or conversational sto-rytelling analysis. Here stories are not reified artifacts and past stuff but living, embodied, collaborative/polyphonic and ongoing process. Conversation analysis is now a well-established framework for examining conversational exchange. But a spontaneous discourse interaction remains out of reach for many computer scientists to capture, represent and share its meaning. Furthermore many elements belonging to any communication process are not 'here and now' present in the situation but also distributed across time and space. Without rejecting the two previous approaches, Ontologies, Graphs and Networks approach coupled with Big Data promise an attractive challenge. We will present our actual work grounded on Pentland and Feldman's (2007) narrative network theoretical framework but completed by additional computing contributions.