Information Technology Reference
In-Depth Information
Structured Prior Knowledge and Granular
Structures
Qinrong Feng
1
,
2
and Duoqian Miao
2
1
School of Mathematics and Computer Science, Shanxi Normal University, Linfen,
Shanxi, 041004, P.R.China
2
Department of Computer Science and Technology, Tongji University, Shanghai,
201804, P.R.China
Abstract.
In this paper, a hierarchical organization of prior knowledge
based on multidimensional data model is firstly proposed, it is the basis
of structured thinking. Secondly, a representation of granular structures
based on multidimensional data model is also proposed, it can represents
information from multiview and multilevel. Finally, the relation between
structured prior knowledge and granular structures is analyzed.
1
Introduction
Granular computing is a general computation theory for effectively using gran-
ules such as classes, clusters, subsets, groups and intervals to build an ecient
computational model for complex applications with huge amounts of data, in-
formation and knowledge [1]. It is also a way of thinking [2] that relies on the
human ability to perceive the real world under various levels of granularity (i.e.,
abstraction).
Hobbs [3] stated that 'We perceive and represent the world under various
grain sizes, and abstract only those things that serve our present interests. The
ability to conceptualize the world at different granularities and to switch among
these granularities is fundamental to our intelligence and flexibility. This enables
us to map the complexities of real world into computationally tractable simpler
theories'. Gordon et al. [4] pointed out that human perception benefits from the
ability to focus attention at various levels of detail and to shift focus from one
level to another. The grain size at which people choose to focus affects not only
what they can discern but what becomes indistinguishable, thus permitting the
mind to ignore confusing detail. Bradley C. Love [5] noticed that humans fre-
quently utilize and acquire category knowledge at multiple levels of abstraction.
Yao [2] proposed the basic ideas of granular computing, i.e., problem solving
with different granularities. Granular computing, as a way of thinking, can cap-
tures and reflects our ability to perceive the world at different granularity and
to change granularities in problem solving.
Although these scholars have noticed that human can solve problem at differ-
ent levels of granularity, the reason of which has seldom been analyzed until now.
If we know how the human intellect works, we could simulate it by machine.
