Knowledge Representation Formalisms

Knowledge Representation Formalisms: Some of the abstract knowledge representation mechanisms are the following:

Simple relational knowledge: The simplest way of storing facts is to use a relational method where each fact about a set of objects is set out systematically in columns. This representation gives little opportunity for inference, but it can be used as the knowledge basis for inference engines.

Simple way to store facts.
Each fact about a set of objects is set out systematically in columns.
Little opportunity for inference.
Knowledge basis for inference engines.

We can ask things like:

Who is dead?
Who plays Jazz/Trumpet etc.?

This sort of representation is popular in database systems.

Inheritable knowledge: Relational knowledge is made up of objects consisting of

attributes
corresponding associated values.

We extend the base more by allowing inference mechanisms:

• Property inheritance

elements inherit values from being members of a class.
data must be organised into a hierarchy of classes.

Boxed nodes -- objects and values of attributes of objects.
Values can be objects with attributes and so on.
Arrows -- point from object to its value.
This structure is known as a slot and filler structure, semantic network or a collection of frames.

The algorithm to retrieve a value for an attribute of an instance object:

Find the object in the knowledge base
If there is a value for the attribute report it
Otherwise look for a value of instance if none fail
Otherwise go to that node and find a value for the attribute and then report it
Otherwise search through using isa until a value is found for the attribute.

Inferential Knowledge: Represent knowledge as formal logic:
All dogs have tails : dog(x) hasatail(x) Advantages:

A set of strict rules.

o Can be used to derive more facts.
o Truths of new statements can be verified.
o Guaranteed correctness.

Many inference procedures available to in implement standard rules of logic.
Popular in AI systems. e.g Automated theorem proving.

Procedural knowledge: Basic idea:

Knowledge encoded in some procedures

o small programs that know how to do specific things, how to proceed.
o e.g a parser in a natural language understander has the knowledge that a noun phrase may contain articles, adjectives and nouns. It is represented by calls to routines that know how to process articles, adjectives and nouns.
Advantages:

Heuristic or domain specific knowledge can be represented.
Extended logical inferences, such as default reasoning facilitated.
Side effects of actions may be modelled. Some rules may become false in time. Keeping track of this in large systems may be tricky.

Disadvantages:

Completeness -- not all cases may be represented.
Consistency -- not all deductions may be correct.