Knowledge Representation Schemes
There are four types of Knowledge representation :
Relational, Inheritable, Inferential, and Declarative/Procedural.
◊ Relational Knowledge :
provides a framework to compare two objects based on equivalent attributes.
any instance in which two different objects are compared is a relational type of knowledge.
is obtained from associated objects.
it prescribes a structure in which new objects are created which may inherit all or a subset of attributes from existing objects.
is inferred from objects through relations among objects.
e.g., a word alone is a simple syntax, but with the help of other words in phrase the reader may infer more from a word; this inference within linguistic is called semantics.
a statement in which knowledge is specified, but the use to which that knowledge is to be put is not given.
e.g. laws, people's name; these are facts which can stand alone, not dependent on other knowledge;
a representation in which the control information, to use the knowledge, is embedded in the knowledge itself.
e.g. computer programs, directions, and recipes; these indicate specific use or implementation;
Relational Knowledge :
This knowledge associates elements of one domain with another domain.
Relational knowledge is made up of objects consisting of attributes and their corresponding associated values.
The results of this knowledge type is a mapping of elements among different domains.
The table below shows a simple way to store facts.
The facts about a set of objects are put systematically in columns. − This representation provides little opportunity for inference.
Table - Simple Relational Knowledge
Given the facts it is not possible to answer simple question such as :
Who is the heaviest player ? ".
but if a procedure for finding heaviest player is provided, then these facts will enable that procedure to compute an answer.
‡ We can ask things like who "bats – left" and "throws – right".
Inheritable Knowledge :
Here the knowledge elements inherit attributes from their parents.
The knowledge is embodied in the design hiera hies found in the functional, physical and process domains. Within the hiera hy, elements inherit attributes from their parents, but in many cases not all attributes of the parent elements be prescribed to the child elements.
The inheritance is a powerful form of inference, but not adequate. The basic KR needs to be augmented with inference mechanism.
The KR in hiera hical structure, shown below, is called “semantic network” or a collection of “frames” or “slot-and-filler structure". The structure shows property inheritance and way for insertion of additional knowledge.
Property inheritance : The objects or elements of specific classes inherit attributes and values from more general classes. The classes are organized in a generalized hierahy.
The directed arrows represent attributes (isa, instance, team) originates at object being described and terminates at object or its value.
The box nodes represents objects and values of the attributes.
◊ Viewing a node as a frame
Example : Baseball-player
isa : Adult-Male Bates : EQUAL handed
Height : 6.1
Batting-average : 0.252
◊ Algorithm : Property Inheritance
Retrieve a value V for an attribute A of an instance object O. Steps to follow:
Find object O in the knowledge base.
If there is a value for the attribute A then report that value.
Else, if there is a value for the attribute instance; If not, then fail.
Else, move to the node corresponding to that value and look for a value for the attribute A; If one is found, report it.
Else, do until there is no value for the “isa” attribute or
until an answer is found :
Get the value of the “isa” attribute and move to that node.
See if there is a value for the attribute A; If yes, report it.
This algorithm is simple. It describes the basic mechanism of inheritance. It does not say what to do if there is more than one value of the instance or “isa” attribute.
This can be applied to the example of knowledge base illustrated, in the previous slide, to derive answers to the following queries :
team (Pee-Wee-Reese) = Brooklyn–Dodger
batting–average(Three-Finger-Brown) = 0.106 − height (Pee-Wee-Reese) = 6.1
bats (Three Finger Brown) = right
Inferential Knowledge :
This knowledge generates new information from the given information.
This new information does not require further data gathering form sou e,
but does require analysis of the given information to generate new
given a set of relations and values, one may infer other values or relations.
a predicate logic (a mathematical deduction) is used to infer from a set of attributes.
inference through predicate logic uses a set of logical operations to relate individual data.
the symbols used for the logic operations are :
Examples of predicate logic statements :
"Wonder" is a name of a dog :
2. All dogs belong to the class of animals : ∀ x : dog (x) → animal(x)
3. All animals either live on land or in ∀ x : animal(x) → live (x,
water : land) V live (x, water)
From these three statements we can infer that :
" Wonder lives either on land or on water."
Note : If more information is made available about these objects and their relations, then more knowledge can be inferred.
Differences between Declarative/Procedural knowledge is not very clear.
Declarative knowledge :
Here, the knowledge is based on declarative facts about axioms and domains .
axioms are assumed to be true unless a counter example is found to invalidate them.
− domains represent the physical world and the pe eived functionality.
axiom and domains thus simply exists and serve as declarative statements that can stand alone.
Here, the knowledge is a mapping process between domains that specify
“what to do when” and the representation is of “how to make it” rather than “what it is”. The procedural knowledge :
may have inferential efficiency, but no inferential adequacy and acquisitional efficiency.
are represented as small programs that know how to do specific things, how to proceed.
Example : A parser in a natural language has the knowledge that a noun phrase may contain articles, adjectives and nouns. It thus accordingly call routines that know how to process articles, adjectives and nouns.
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