By Gian Piero Zarri (auth.)
ISBN-10: 1848000774
ISBN-13: 9781848000773
A substantial volume of significant, economically suitable details, is buried in the large mass of multimedia records that correspond to a couple kind of ‘narrative’ description.
Due to the ubiquity of those narrative assets, representing in a common, exact, and potent method their semantic content material – i.e., their key ‘meaning’ – is then either conceptually suitable and economically very important. This booklet provides the most houses of NKRL (‘Narrative wisdom illustration Language’), a language expressly designed for representing and coping with, in a standardised method, the which means of complicated multimedia narrative files. NKRL is an absolutely carried out language/environment that exists in models: a relational database-supported model and a file-oriented one. It constitutes the most whole and practical attempt realised up to now to accommodate the large business prospects of the narrative domain.
Written from a multidisciplinary standpoint, this ebook not just provides an exhaustive description of NKRL and of the linked wisdom illustration ideas, it additionally constitutes a useful resource of reference for practitioners, researchers and graduates in domain names that variety over narrative theories, linguistics and computational linguistics, synthetic intelligence, wisdom bases, info retrieval, ontologies and the semantic Web.
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Extra info for Representation and management of narrative information: theoretical principles and implementation
Sample text
In the full notation, the PRED link relates the central proposition node (represented by a circle) to a predicative node (GAVE), where a suitable number of links to concept nodes like Joe and Mary denote the arguments of the predicate. The latter are marked by letters like A, B and C that correspond clearly to roles; it is interesting to note that the ‘‘simplified’’ representation of Fig. 6 matches exactly the (very recent) W3C representation proposed for n-ary relations; see Fig. 2. Among the current n-ary solutions of AI origin we can mention Topic Maps (TMs).
2 and described in detail Chapter 3 – and despite the title of one of Sowa’s papers, ‘‘Using a lexicon of canonical graphs in a semantic interpreter’’ [Sowa, 1988], an exhaustive and authoritative list of these structures under the form of ‘‘canonical graphs’’ does not exist, and its construction seems never to have been planned. The practical consequence of this state of affairs, then, is the need, whenever a concrete application of CGs theory must be implemented, to define anew a specific list of ‘‘canonical graphs’’ for this particular application.
In spite of some possible, ‘‘minor’’ representational defaults of CGs – like a particularly convolute solution to the problem of the connectivity phenomena (interrelation of graphs) or some ambiguities about the exact ‘‘roles’’ of some conceptual relations – it is easy to see that CGs and NKRL share many representational problems and many of the corresponding solutions. However, the ‘‘general philosophy’’ of the two approaches is very different. Even without bearing in mind the fact that CGs present themselves like a sort of ‘‘universal’’ system, while NKRL is focused on the solution of the specific ‘‘narrative’’ problems, it seems evident that work in a CG context concerns mainly the ‘‘academic’’ domain and that NKRL work, on the contrary, has more of an ‘‘engineering’’ and ‘‘practical’’ flavor.
Representation and management of narrative information: theoretical principles and implementation by Gian Piero Zarri (auth.)
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