Issues in Knowledge Representation
The fundamental goal of Knowledge
Representation is to facilitate inferencing (conclusions) from knowledge.
The issues that arise while using
KR techniques are many. Some of these are explained below.
Important Attributes :
Any attribute of objects so basic
that they occur in almost every problem domain ?
Relationship among attributes:
Any important relationship that
exists among object attributes ?
Choosing Granularity :
At what level of detail should
the knowledge be represented ?
Set of objects :
How sets of objects be
represented ?
Finding Right structure :
Given a large amount of knowledge
stored, how can relevant parts be accessed ?
Important Attributes :
There are attributes that are of
general significance.
There are two attributes "instance" and "isa", that are of general
importance. These attributes are important because they support property inheritance.
Relationship among Attributes : (Ref. Example- Fig. Inheritable
KR)
The attributes to describe objects
are themselves entities they represent.
The relationship between the
attributes of an object, independent of specific knowledge they encode, may
hold properties like:
Inverses, existence in an isa hiera hy, techniques for reasoning
about values and single valued attributes.
◊ Inverses :
This is about consistency check,
while a value is added to one attribute. The entities are related to each other
in many different ways. The figure shows attributes (isa, instance, and team), each with a directed arrow, originating
at the object being described and terminating either at the object or its
value.
There are two ways of realizing
this:
first, represent two
relationships in a single representation;
e.g., a logical representation, team(Pee-Wee-Reese, Brooklyn–Dodgers), that can be interpreted as a
statement about Pee-Wee-Reese or Brooklyn–Dodger.
second, use attributes that focus
on a single entity but use them in
pairs,
one the inverse of the other; for e.g., one,
Dodgers , and the other, team = Pee-Wee-Reese, . . . .
This second approach is followed
in semantic net and frame-based systems, accompanied by a knowledge acquisition
tool that guarantees the consistency of inverse slot by checking, each time a
value is added to one attribute then the corresponding value is added to the
inverse.
◊ Existence in an "isa" hiera hy :
This is about generalization-specialization,
like, classes of objects and specialized subsets of those classes. There are
attributes and specialization of attributes.
Example: the attribute "height" is a specialization of
general attribute
"physical-size" which is, in turn, a specialization of "physical-attribute". These generalization-specialization relationships for
attributes are important because they support inheritance.
Techniques for reasoning about values :
This is about reasoning values of attributes not given explicitly. Several kinds of information are used in reasoning, like,
height : must be in a unit of
length,
age : of person can not be
greater than the age of person's parents.
The values are often specified
when a knowledge base is created.
Single valued attributes :
This is about a specific attribute that is guaranteed to take a unique value.
Example : A baseball player can
at time have only a single height and be a member of only one team. KR systems
take different approaches to provide support for single valued attributes.
Choosing Granularity
What level should the knowledge be
represented and what are the primitives ?
Should there be a
small number or should there be a large number of low-level primitives or
High-level facts.
High-level facts may
not be adequate for inference while Low-level primitives may require a lot of
storage.
Example of Granularity :
Suppose we are interested in
following facts
John spotted Sue.
This could be represented as
Spotted (agent(John), object (Sue))
Such a representation would make
it easy to answer questions such are
Who spotted Sue ?
Suppose we want to know
Did John see Sue ?
Given only one fact, we cannot
discover that answer.
We can add other facts, such as
Spotted (x , y) → saw (x , y)
We can now infer the answer to
the question.
Set of
Objects KR - issues
Certain properties
of objects that
are true as member of
a set but not as individual;
Example :
Consider the assertion made in the sentences
"there
are more sheep than people in Australia", and "English speakers can be found all over the world."
To describe these facts, the only
way is to attach assertion to the sets representing people, sheep, and English.
The
reason to represent sets of objects is :
If a property is true for all or
most elements of a set, then it is more efficient to associate it once with the
set
rather than to associate it
explicitly with every elements of the set . This is done in different ways :
in logical representation through
the use of universal quantifier, and
in hiera hical structure where
node represent sets, the inheritance propagate set level assertion down to
individual.
Example: assert large (elephant);
Remember to make clear distinction
between,
whether we are asserting some
property of the set itself,
means, the set of elephants is large, or
asserting some property that holds
for individual elements of the set , means, any thing that is an elephant is
large.
There are three ways in which sets
may be represented :
Name, as in the example – Ref
Fig. Inheritable KR, the node - Baseball-Player and the predicates as Ball and
Batter in logical representation.
Extensional definition is to list
the numbers, and
In tensional definition is to
provide a rule, that returns true or
false depending on whether the
object is in the set or not.
Finding Right Structure
Access to right structure for
describing a particular situation.
It requires, selecting an initial
structure and then revising the choice. While doing so, it is necessary to
solve following problems :
how to
perform an initial selection of the most appropriate structure.
how to fill
in appropriate details from the current situations.
how to find a better structure if
the one chosen initially turns out not to be appropriate.
what to do if none of the available
structures is appropriate.
when to create and remember a new
structure.
There is no good, general purpose
method for solving all these problems. Some knowledge representation techniques
solve some of them.
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