Two orthogonal ways of implementing ‘reasoning’ in the knowledge-based systems. In the first way – developed originally in a automatic theorem-proving context – the result of the inference operations is the deduction of new facts (new knowledge) from the existing information. It is, then, the privileged way of setting up ‘rules’. In the second, weaker way – used, e.g., as ‘native’ reasoning tool in the so-called W3C languages (RDF(S), OWL) – no new knowledge is produced from the pre-existing one, and the reasoning techniques are only used to solve some classification problem when setting up well-formed ontologies. Typical tasks in this context are, e.g., i) checking the consistency of classes/concepts (i.e., determining whether a class can have any instances), and ii) calculating the subsumption hierarchy (i.e., arranging the classes according to their generic/specific relationships).
Published in Chapter:
Using Rules in the Narrative Knowledge Representation Language (NKRL) Environment
Gian Piero Zarri (University Paris Est and LISSI Laboratory, France)
Copyright: © 2009
|Pages: 26
DOI: 10.4018/978-1-60566-402-6.ch003
Abstract
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.