Basically, directed graphs (digraphs) where the nodes represent concepts, and the arcs different kinds of associative links, not only the ‘classical’ IsA and property-value links, but also, e.g., ‘ternary’ relationships derived from Case Grammar in Linguistics and labeled as Actor, Object, Recipient, Instrument etc. Representational solutions that can be reduced in some way to a Semantic Network framework include, among (many) other things, Ceccato’s Correlational Grammar – which goes back to the fifties – Quillian’s Semantic Memory, Schank's Conceptual Dependency theory, Sowa’s Conceptual Graphs, Lenat’s CYC, Zarri’s NKRL (Narrative Knowledge Representation Language), etc. Semantic Network solutions have been often used/proposed to represent different kinds of narrative phenomena.
Published in Chapter:
Representation Languages for Unstructured ‘Narrative’ Documents
Gian Piero Zarri (University Paris Est and LISSI Laboratory, France)
Copyright: © 2011
|Pages: 14
DOI: 10.4018/978-1-59904-931-1.ch132
Abstract
A big amount of important, ‘economically relevant’ information, is buried into unstructured, multimedia ‘narrative’ resources. This is true, e.g., for most of the corporate knowledge documents (memos, policy statements, reports, minutes etc.), for the news stories, the normative and legal texts, the medical records, many intelligence messages, the ‘storyboards/historians’ describing sequences of events in industrial plants, the surveillance videos, the actuality photos for newspapers and magazines, lot of material (text, image, video, sound…) for eLearning etc., as well as, in general, for a huge fraction of the information stored on the Web. In these ‘narrative documents’, or ‘narratives’, the main part of the information content consists in the description of ‘events’ that relate the real or intended behavior of some ‘actors’ (characters, personages, etc.) – the term ‘event’ is taken here in its more general meaning, covering also strictly related notions like fact, action, state, situation etc. These actors try to attain a specific result, experience particular situations, manipulate some (concrete or abstract) materials, send or receive messages, buy, sell, deliver etc. Note that, in these narratives, the actors or personages are not necessarily human beings; we can have narrative documents concerning, e.g., the vicissitudes in the journey of a nuclear submarine (the ‘actor’, ‘subject’ or ‘personage’) or the various avatars in the life of a commercial product. Note also that, even if a large amount of narrative documents concerns natural language (NL) texts, this is not necessarily true, and ‘narratives’ are really ‘multimedia’. A photo representing a situation that, verbalized, could be expressed as “The US President is addressing the Congress” is not of course an NL text, yet it is still a narrative document. Because of the ubiquity of these ‘narrative’ resources, being able to represent in a general, accurate, and effective way their semantic content – i.e., their key ‘meaning’ – is then both conceptually relevant and economically important: narratives form, in fact, a huge underutilized component of organizational knowledge, and people could be willing to pay for a system able to process in an ‘intelligent’ way this information and/or for the results of the processing. This type of explicit yet unstructured knowledge can be, of course, indexed and searched in a variety of ways, but is requires, however, an approach for formal analysis and effective utilization that is neatly different from the ‘traditional’ ones.