NKRL, a knowledge representation tool for encoding the ‘meaning’ of complex narrative texts (original) (raw)
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A Glimpse of NKRL, the "Narrative Knowledge Representation Language
2000
In this paper, I describe NKRL, a language expressly designed for representing, in a standardised way, the semantic content (the "meaning") of complex narrative texts. After having introduced the four "components" (specialised sub-languages) of NKRL, I will give some examples of its practical modalities of use. I will then describe, in a very sketchy way, the inference techniques and the
NKRL, a knowledge representation language for narrative natural language processing
Proceedings of the 16th conference on Computational linguistics -, 1996
NKRL is a conceptual language which intends to provide a normalised, pragmatic description of the semantic contents (in short, the "meaning") of NL narrative documents. We introduce firstly the general architecture of NKRL, and we give some examples of its characteristic features. We supply, afterward, some sketchy information about the inference techniques and the NLP procedures associated with this language.
An n-ary language for representing narrative information on the web
SWAP, 2005
In this paper, we evoke first the ubiquity and the importance of the so-called 'narrative' information, showing that the usual ontological tools, both the 'traditional' and the 'Semantic Web' ones, are unable to offer complete and reliable solutions for representing and exploiting this type of information. We supply then some details about NKRL (Narrative Knowledge Representation Language), a knowledge representation and inferencing environment especially created for an 'intelligent' exploitation of narrative knowledge.
Towards Narrative-Based Knowledge Representation in Cognitive Systems
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The hypothesis according to which narrative is not only a prominent form of human com- munication but also a fundamental way to represent knowledge and to structure the mind has been limitedly but increasingly discussed for the last 40 years. However, in the realm of Artificial Intelligence, it did not lead to an elaborate model of knowledge representation, beyond scripts and cases. In this paper, we attempt to go further by identifying three differentiating features of narratives that may inspire novel forms of knowledge representation: transformation, conflict and unactualized events. In particular, these three features open the way for knowledge representation formalisms that take greater account of the co-existence of intertwined conflicting representations, with various validities and validity domains, beyond a purely factual representation of the world.
Data Analytics in Digital Humanities, 2017
In this chapter, we describe the conceptual tools that, in an NKRL context (NKRL = Narrative Knowledge Representation Language), allow us to obtain a (computer-usable) description of full “narratives” as logically structured associations of the constituting (and duly formalized) “elementary events.” Dealing with this problem means, in practice, being able to formalize those “connectivity phenomena”—denoted, at “surface level,” by logico-semantic coherence links like causality, goal, co-ordination, subordination, indirect speech, etc.—that assure the conceptual unity of a whole narrative. The second-order, unification based solutions adopted by NKRL in this context, “completive construction” and “binding occurrences,” allow us to take into account the connectivity phenomena by “reifying” the formal representations used to model the constitutive elementary events. These solutions, which are of interest from a general digital humanities point of view, are explained in some depth making use of several illustrating examples.
Using Rules in the Narrative Knowledge Representation Language (NKRL) Environment
Open Solutions and Approaches, 2009
NKRL is a semantic language expressly designed to deal with all sort of 'narratives', in particular with those ('non-fictional narratives') of an economic interest. From a knowledge representation point of view, its main characteristics consists in the use of two different sorts of ontologies, a standard, binary ontology of concepts, and an ontology of n-ary templates, where each template corresponds to the formal representation of a class of elementary events. Rules in NKRL correspond to high-level reasoning paradigms like the search for causal relationships or the use of analogical reasoning. Given i) the conceptual complexity of these paradigms, and ii) the sophistication of the underlying representation language, rules in NKRL cannot be implemented in a (weak) 'inference by inheritance' style but must follow a powerful 'inference by resolution' approach. After a short reminder about these two inference styles, and a quick introduction of the NKRL language, the chapter describes in some depth the main characteristics of the NKRL inference rules.
Mining Knowledge in Storytelling Systems for Narrative Generation
Proceedings of the INLG 2016 Workshop on Computational Creativity in Natural Language Generation, 2016
Storytelling systems are computational systems designed to tell stories. Every story generation system defines its specific knowledge representation for supporting the storytelling process. Thus, there is a shared need amongst all the systems: the knowledge must be expressed unambiguously to avoid inconsistencies. However, when trying to make a comparative assessment between the storytelling systems, there is not a common way for expressing this knowledge. That is when a form of expression that covers the different aspects of the knowledge representations becomes necessary. A suitable solution is the use of a Controlled Natural Language (CNL) which is a good halfway point between natural and formal languages. A CNL can be used as a common medium of expression for this heterogeneous set of systems. This paper proposes the use of Controlled Natural Language for expressing every storytelling system knowledge as a collection of natural language sentences. In this respect, an initial grammar for a CNL is proposed, focusing on certain aspects of this knowledge.