A Conceptual Methodology for Dealing with Terrorism "Narratives"

2010 ◽  
Vol 2 (2) ◽  
pp. 47-63
Author(s):  
Gian Piero Zarri

This paper concerns the use of in-depth analytical/conceptual techniques pertaining to the Artificial Intelligence domain to deal with narrative information (or “narratives”) in the terrorism- and crime-related areas. More precisely, the authors supply details about NKRL (Narrative Knowledge Representation Language), a representation and querying/inferencing environment especially created for an advanced exploitation of all types of narrative information. This description will be integrated with concrete examples that illustrate the use of NKRL tools in two recent ‘defence’ applications, the first dealing with a corpus of “Southern Philippines terrorism” news stories used in an R&D European project, the second, carried out in collaboration with the French “Délégation Générale pour l’Armement” (DGA, Central Bureau for Armament), which handles news stories about Afghanistan’s war.

Author(s):  
Gian Piero Zarri

This paper concerns the use of in-depth analytical/conceptual techniques pertaining to the Artificial Intelligence domain to deal with narrative information (or “narratives”) in the terrorism- and crime-related areas. More precisely, the authors supply details about NKRL (Narrative Knowledge Representation Language), a representation and querying/inferencing environment especially created for an advanced exploitation of all types of narrative information. This description will be integrated with concrete examples that illustrate the use of NKRL tools in two recent ‘defence’ applications, the first dealing with a corpus of “Southern Philippines terrorism” news stories used in an R&D European project, the second, carried out in collaboration with the French “Délégation Générale pour l’Armement” (DGA, Central Bureau for Armament), which handles news stories about Afghanistan’s war.


Author(s):  
TRU H. CAO

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.


1997 ◽  
Vol 3 (2) ◽  
pp. 231-253 ◽  
Author(s):  
GIAN PIERO ZARRI

In this paper, we describe NKRL (Narrative Knowledge Representation Language), a language designed for representing, in a standardized way, the semantic content (the ‘meaning’) of complex narrative texts. After having introduced informally the four ‘components’ (specialized sub-languages) of NKRL, we will describe (some of) the data structures proper to each of them, trying to show that the NKRL coding retains the main informational elements of the original narrative expressions. We will then focus on an important subset of NKRL, the so-called AECS sub-language, showing in particular that the operators of this sub-language can be used to represent some sorts of ‘plural’ expressions.


2014 ◽  
Vol 14 (4-5) ◽  
pp. 587-601 ◽  
Author(s):  
MICHAEL GELFOND ◽  
YUANLIN ZHANG

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.


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