Exploiters-Based Knowledge Extraction in Object-Oriented Knowledge Representation
This work addresses knowledge extraction for object-oriented dynamic networks, but it appears incremental as it builds on existing models like frames and object-oriented programming.
The paper tackles the problem of knowledge extraction in object-oriented knowledge representation by introducing a new exploiters-based approach that generates a finite set of new object classes from a basic set, with methods to calculate the quantity of new classes and types before generation.
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.