Exploiters-Based Knowledge Extraction in Object-Oriented Knowledge Representation

TL;DR

This paper introduces a novel exploiters-based knowledge extraction method for object-oriented knowledge representation models, enabling finite and calculable generation of new object classes from basic sets.

Contribution

It proposes a new approach using exploiters to generate a finite set of new classes, with methods to calculate their quantity and types, and proves the formation of an upper semilattice.

Findings

The approach guarantees finitely defined new classes for any object-oriented dynamic network.

The number of generated classes can be precisely calculated beforehand.

The method preserves the basic class set in the knowledge base.

Abstract

This paper contains the consideration of knowledge extraction mechanisms of such object-oriented knowledge representation models as frames, object-oriented programming and object-oriented dynamic networks. In addition, conception of universal exploiters within object-oriented dynamic networks is also discussed. The main result of the paper is introduction of new exploiters-based knowledge extraction approach, which provides generation of a finite set of new classes of objects, based on the basic set of classes. The methods for calculation of quantity of new classes, which can be obtained using proposed approach, and of quantity of types, which each of them describes, are proposed. Proof that basic set of classes, extended according to proposed approach, together with union exploiter create upper semilattice is given. The approach always allows generating of finitely defined set of new classes of objects for any object-oriented dynamic network. A quantity of these classes can be precisely calculated before the generation. It allows saving of only basic set of classes in the knowledge base.