Using Rules in the Narrative Knowledge Representation Language (NKRL) Environment (original) (raw)
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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
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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.
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International journal of robotic computing, 2019
We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/ narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an ontology of events to the usual ontology of objects), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of rule used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced types of inference operations based, e.g., on analogical and causal reasoning.
Advanced computational reasoning based on the NKRL conceptual model
Expert Systems with Applications, 2013
After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of ''rules''-e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWLthis paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL's methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an 'intelligent' and complete way, with complex and content-rich non-fictional 'narrative' data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between ''plain/static'' and ''structured/dynamic'' knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of ''functional roles''-associated with the possibility of making use of n-ary structures-allows us to build up highly 'expressive' rules whose ''atoms'' can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, ''functional roles'' are primitive symbols interpreted as ''relations''-like ''subject'', ''object'', ''source'', ''beneficiary'', etc.-that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the ''semantic roles'' that are equated to ordinary concepts like ''student'', to be inserted into the ''non-sortal'' (no direct instances) branch of a traditional ontology.
Functional and semantic roles in a high-level knowledge representation language
Artificial Intelligence Review
We describe in this paper a formalization of the notion of "role" that involves a clear separation between two very different sorts of roles. Semantic roles, like student or customer, are seen as (pre-defined) transitory properties that can be associated with (usually animate) entities. From a formal point of view, they can be represented as standard concepts to be placed into a specific branch of a particular ontology; they formalize the static and classificatory aspects of the notion of role. Functional roles must be used, instead, to model those pervasive and dynamic situations corresponding to events, activities, circumstances etc. that are characterized by spatio-temporal references; see, e.g., "John is now acting as a student". They denote the specific function with respect to the global meaning of an event/situation/activity… that is performed by the entities involved in this event/situation... and formalize the dynamic and relational aspects of the notion of role. A functional role of the subject/agent/actor/protagonist… type is used to associate "John" with the notion of student or customer (semantic roles) during a specific time interval. Formally, functional roles are expressed as primitive symbols like subject, object, source, beneficiary. Semantic and functional roles interact smoothly when they are used to deal with challenging knowledge representation problems like the so-called "counting problem", or when we need to setup powerful inference rules whose atoms can directly denote complex situations. In this paper, the differentiation between semantic and functional roles will be illustrated from an NKRL point of view. NKRL (Narrative Knowledge Representation Language) is a high-level conceptual tool used for the computer-usable representation and management of the inner meaning of syntactically complex and semantically rich multimedia information. But, as we will see, the importance of this distinction goes well beyond its usefulness in a specific NKRL context. In particular, the use of functional roles is of paramount importance for the setup of those evolved n-ary forms of knowledge representation that allow us to get rid from the limitations in expressiveness proper to the standard (binary) solutions.
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.
Semantic Annotation Using NKRL (Narrative Knowledge Representation Language)
2004
We suggest that it could be possible to come closer to the Semantic Web goals by using 'semantic annotations' that enhance the traditional ontology paradigm by supplementing the ontologies of concepts with 'ontologies of events'. We present then some of the properties of NKRL (Narrative Knowledge Representation Language), a conceptual modeling formalism that makes use of ontologies of events to annotate in great detail those 'narratives' that represent a very large percentage of the global Web information.