Information Technology Reference
In-Depth Information
Table 4.1
The roles of knowledge representation. (Davis et al.
1993
)
Role
Description
Surrogate
A substitute for the thing itself. For reasoning about the world
rather than taking action in it
Set of ontological
commitments
In which terms should the world be thought about?
A fragmentary theory of
intelligent reasoning
Expressed in terms of:
1. The conception of reasoning
2. The set of inferences sanctioned
3. The set of inferences recommended
A medium for effective
computing
1. An environment in which thinking is accomplished
2. Guidance for organising information
A medium for human
expression
A language which says things about the world
here is that it is a serious attempt at categorising knowledge by the role it has within
a human, social and technological framework. However, the presumption here is that
knowledge remains only within the human domain, and that it is a system whose
parts must necessarily remain within that human world.
This restriction to the human domain is explicitly made through the warning,
“
A knowledge representation is not a data structure
”, where data structure may be
some abstract representation scheme (such as a graph), usually created for eventual
computer storage (Addis
1985
). However, it is generally accepted that a 'semantic
net', which is a directed graph of annotated boxes and arrows, does represent some
components of knowledge. The reason for this distinction seems to be based upon the
idea that semantic nets have 'semantics', where 'semantics' refers to the topological
constraints that come from what the net represents rather than its construction rules.
This loses a clear understanding of what the difference is between nets and graphs
since the difference is identified through a set of unspecified implicit constraints
that are only obtainable through human interpretation. This pushes the nature of
knowledge back from whence it came, into the minds of peoples, so calling this
graphical representation a 'semantic' net does not, in itself, give it meaning.
If a proper study of knowledge is to be undertaken then the relationship between a
representation and the world it represents must be investigated. What will need to be
considered now is the place that a users of a representation have with respect to their
interpretation of. It is only then that an understanding of engineering Knowledge
Systems can evolve.
My theory of knowledge, expressed in part in this chapter, addresses this problem
of engineering knowledge systems. This will include some mention of machine
learning and scientific discovery (Addis
1985
,
1989
,
1990
). The theory is based
upon a knowledge taxonomy that identifies the different types of knowledge called
into play when a system has to interact with the world to achieve some purpose.
The taxonomy has been derived from how it is discussed and used by knowledge
engineers. Thus, knowledge in this chapter is classified according to its role in a
