UDC 007:681.512.2
HIERARCHIC NUMBERS FOR THE DESIGN OF ICF-TAXONOMIES ARTIFICIAL INTELLIGENCE
I. Yu. Kashirin, Dr. Sc. (Tech.), full professor, RSREU, Ryazan, Russia,
orcid.org/0000-0002-6256-6558, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
The most important task of designing taxonomies for knowledge models of artificial intelligence is considered. As a basic taxonomy relation, ICF adjacency relation is proposed to be used. Such an approach makes it possible to efficiently design general and applied ontologies for solving problems of artificial intelligence. The article introduces original mathematical apparatus of hierarchical numbers, which makes it possible to analyze general ICF ontologies by calculating the main characteristics of taxonomic hierarchies. Such characteristics include, for example, generality measure for two or more concepts or the complexity of structural path in a taxonomic hierarchy between two concepts. As an example, confirming the possibility of the effective use of ICF taxonomies in solving real problems of artificial intelligence, general taxonomy with the name «spatial situation» is proposed. It shows the possibility of using heuristic programming tasks in intelligent solvers and in intelligent systems to search information resources in global networks, for example, in Semantic Web technology. In the experimental part, to test the capabilities of information retrieval in global networks based on Semantic Web technology using ICF taxonomy, ICF PUTE v.2.02 (ICF polymorphic unification tools environment) software system is considered. ICF PUTE system implements the search in global networks using the means of structuring and unification to work with the content of global networks. The purpose of this subsystem is to verify the operation of the means to structure ICF ontologies for solving applied search problems. The aim of the work is to analyze design features of general and applied ICF ontologies using hierarchical number apparatus for the effective solution of urgent problems of artificial intelligence.
Key words: knowledge models, artificial intelligence, ICF ontology, ontological taxonomies, hierarchical numbers, instrumental software, intelligent problem solvers, Semantic Web technology, information retrieval in global networks, generic and causal relationships, dichotomy, polymorphic representation of knowledge.