  A conceptual system with rich connotation is key to improving the performance
of knowledge-based artificial intelligence systems. While a conceptual system,
which has abundant concepts and rich semantic relationships, and is
developable, evolvable, and adaptable to multi-task environments, its actual
construction is not only one of the major challenges of knowledge engineering,
but also the fundamental goal of research on knowledge and conceptualization.
Finding a new method to represent concepts and construct a conceptual system
will therefore greatly improve the performance of many intelligent systems.
Fortunately the core of human cognition is a system with relatively complete
concepts and a mechanism that ensures the establishment and development of the
system. The human conceptual system can not be achieved immediately, but rather
must develop gradually. Developmental psychology carefully observes the process
of concept acquisition in humans at the behavioral level, and along with
cognitive psychology has proposed some rough explanations of those
observations. However, due to the lack of research in aspects such as
representation, systematic models, algorithm details and realization, many of
the results of developmental psychology have not been applied directly to the
building of artificial conceptual systems. For example, Karmiloff-Smith's
Representation Redescription (RR) supposition reflects a concept-acquisition
process that re-describes a lower level representation of a concept to a higher
one. This paper is inspired by this developmental psychology viewpoint. We use
an object-oriented approach to re-explain and materialize RR supposition from
the formal semantic perspective, because the OO paradigm is a natural way to
describe the outside world, and it also has strict grammar regulations.
