"Narrative" Information and the NKRL Solution

"Narrative" Information and the NKRL Solution

Gian Piero Zarri (LaLIC, University Paris 4-Sorbonne, France)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-59904-849-9.ch169
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In a companion article of this Encyclopaedia: ‘Narrative’ Information, the Problem, we have introduced the problem of finding a complete and computationally efficient system for representing and managing ‘nonfictional narrative information’. We have stressed there the important economic value of this multimedia type of information – that concerns, e.g., corporate memory documents, news stories, normative and legal texts, medical records, intelligence messages, surveillance videos or visitor logs, actuality photos, eLearning and Cultural Heritage material, etc. We have also emphasised that the usual Computer Science tools – including those pertaining to the now very popular ‘Semantic Web’ domain, see (Bechhofer et al., 2004, Beckett, 2004) – are not really suitable for dealing with this type of information.
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In this article, we will present an Artificial Intelligence tool, NKRL (Narrative Knowledge Representation Language) that has been especially developed for dealing in an ‘intelligent’ way with the nonfictional narrative information. NKRL is, at the same time:

  • a knowledge representation system for describing in the best possible detail the essential content (the ‘meaning’) of complex nonfictional ‘narratives’;

  • a system of reasoning (inference) procedures that, thanks to the richness of the representation system, is able to automatically establish ‘interesting’ relationships among the represented data;

  • an implemented software environment that allows the user to encode the original narratives in terms of the representation language to create ‘NKRL knowledge bases’ in a specific application domain and to exploit ‘intelligently’ these bases.

The main innovation introduced by NKRL with respect to the usual ontological paradigms concerns the addition to the traditional ontology of concepts – called HClass, ‘hierarchy of classes’ in the NKRL’s jargon – an ontology of events, i.e., a new sort of hierarchical organization where the nodes correspond to n-ary structures called ‘templates’ (HTemp, ‘hierarchy of templates’). A partial image of the ‘upper level’ of HClass – that follows then the standard Protégé approach, see (Noy et al., 2000) – is given in Figure 1; for HTemp, see Table 1 and Figure 2 below.

Figure 1.

A partial representation of the ‘upper level’ of HClass, the NKRL ‘traditional’ ontology of concepts.

Figure 2

A. Partial representation of the PRODUCE branch of HTemp, the ‘ontology of events’


A Short Description Of Nkrl

Instead of using the traditional (binary) attribute/value organization, the templates are generated from the n-ary combination of quadruples connecting together the symbolic name of the template, a predicate, and the arguments of the predicate introduced by named relations, the roles. The quadruples have in common the name and predicate components. Denoting then with Li the generic symbolic label identifying a given template, with Pj the predicate used in the template, with Rk the generic role and with ak the corresponding argument, the core data structure for templates has the following general format (see also the companion article, ‘Narrative’ Information, the Problem):

(Li (Pj (R1 a1) (R2 a2) … (Rn an))) (1)

Key Terms in this Chapter

Attributive Operator: The ‘attributive operator’, SPECIF(ication), is one of the four operators used in NKRL for the construction of ‘structured arguments’ (‘complex fillers’ or ‘expansions’) of the conceptual predicates. The SPECIF lists, with syntax (SPECIF ei p1 … pn), are used to represent the properties or attributes that can be asserted about the first element ei, concept or individual, of the list

Ontology of Concepts vs. Ontology of Events: The ontologies of concepts concern the ‘standard’ hierarchical organizations of concepts to be used to model (in a ‘static’ way) a given domain. NKRL adds an ‘ontology of events’, i.e., a new sort of hierarchical organization where the nodes, represented by n-ary structures called ‘templates’, represent general classes of ‘dynamical’ events like “move a physical object”, “produce a service”, “send/receive a message”, etc

Format of NKRL Templates: Templates take the form of n-ary combinations of quadruples connecting together the ‘symbolic name’ of the template, a ‘conceptual predicate’ and the ‘arguments’ of the predicate introduced by named relations, the ‘roles’ (like SUBJ(ect), OBJ(ect), SOURCE, BEN(e)F(iciary), etc.). The quadruples have in common the ‘name’ and ‘predicate’ components. Denoting then with Li the symbolic label identifying the template, with Pj the predicate, with Rk the generic role and with ak the generic argument, the core data structure for templates has the format (Li (Pj (R1 a1) (R2 a2) … (Rn an))).Templates are included in an inheritance hierarchy, HTemp(lates), which implements NKRL’s ‘ontology of events’

NKRL Inference Engine: A software module that carries out the different ‘reasoning steps’ included in hypotheses or transformations. It allows us to use these two classes of inference rules also in an ‘integrated’ mode, augmenting then the possibility of finding interesting (implicit) information

NKRL Inference Rules, Transformations: These rules try to ‘adapt’, from a semantic point of view, a query that failed to the contents of the existing knowledge bases. The principle employed consists in using rules to automatically ‘transform’ the original query into one or more different queries that are not strictly ‘equivalent’ but only ‘semantically close’ to the original one

Binding Occurrences: Second order structures used to deal with those ‘connectivity phenomena’ that arise when several elementary events are connected through causality, goal, indirect speech etc. links. They consists of lists of symbolic labels (ci) of predicative occurrences

NKRL Inference Rules, Hypotheses: They are used to build up automatically ‘reasonable’ connections among the information stored in an NKRL knowledge base according to a number of pre-defined reasoning schemata, e.g., ‘causal’ schemata’

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