Authors:
Ephraim Nissan
1
and
Yaakov HaCohen-Kerner
2
Affiliations:
1
Univ. of London, United Kingdom
;
2
Jerusalem College of Technology (Machon Lev), Israel
Keyword(s):
Information extraction, Explanation generation, Story generation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Information retrieval (IR) and, all the more so, knowledge discovery (KD), do not exist in isolation: it is necessary to consider the architectural context in which they are invoked in order to fulfil given kinds of tasks. This paper discusses a retrieval-intensive context of use, whose intended output is the generation of narrative explanations in a non-bona-fide, entertainment mode subject to heavy intertextuality and strictly constrained by culture-bound poetic conventions. The GALLURA project, now in the design phase, has a multiagent architecture whose modules thoroughly require IR in order to solve specialist subtasks. By their very nature, such subtasks are best subserved by efficient IR as well as mining capabilities within large textual corpora, or networks of signifiers and lexical concepts, as well as databases of narrative themes, motifs and tale types. The state of the art in AI, NLP, story-generation, computational humour, along with IR and KD, as well as the lessons of
the DARSHAN project in a domain closely related to GALLURA’s, make the latter’s goals feasible in principle.
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