Object-Oriented Knowledge Extraction using Universal Exploiters
This work addresses knowledge extraction problems in AI/ML systems, but appears incremental as it builds on existing exploiters-based methods.
The paper tackles knowledge extraction by extending exploiters-based methods to generate new classes and relations, forming a complete lattice. It enables computing quantities of new classes and types, constructing class hierarchies with subsumption relations, and improving inheritance and database knowledge restoration.
This paper contains analysis and extension of exploiters-based knowledge extraction methods, which allow generation of new knowledge, based on the basic ones. The main achievement of the paper is useful features of some universal exploiters proof, which allow extending set of basic classes and set of basic relations by finite set of new classes of objects and relations among them, which allow creating of complete lattice. Proposed approach gives an opportunity to compute quantity of new classes, which can be generated using it, and quantity of different types, which each of obtained classes describes; constructing of defined hierarchy of classes with determined subsumption relation; avoidance of some problems of inheritance and more efficient restoring of basic knowledge within the database.