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Knowledge Representation
Definition and
Technologies
Outline
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What is Knowledge?
What is a Knowledge Representation?
Knowledge Representation in Artificial
Intelligence
Knowledge Representation Technologies
1. What is Knowledge?
• data – primitive verifiable facts, of any
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representation
information – interpreted data
knowledge – relation among sets of data
(information), used for further information
deduction.
• Knowledge is (unlike data) general.
• Knowledge contains information about
behaviour of abstract models of the world.
2. What is a Knowledge
Representation?
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A KR is a Surrogate
A KR is a Set of Ontological Commitments
A KR is a Fragmentary Theory Of Intelligent
Reasoning
A KR is a Medium for Efficient Computation
A KR is a Medium of Human Expression
3. Knowledge Representation in
Artificial Intelligence
Logical AI
Search
Pattern
Recognition
Knowledge
Representation
Inference
Common Sense
Knowledge and
Reasoning
Learning from
Experience
Ontology
Heuristics
Genetic
Programming
4. Knowledge Representation
Technologies
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Logic based representation – first order
predicate logic, Prolog
Procedural representation – rules, production
system
Network representation – semantic networks,
conceptual graphs
Structural representation – scripts, frames,
objects
4.1. Logic based Representation
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First Order Predicate Logic
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enriched by variables, predicates, functions and
quantifiers , 
logic programming
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backwards-chaining implementation of inference (FOPL
and resolution)
question answering
PROLOG: horn-clause logic, no negation, backward
chaining with depth first search
4.2. Procedural Representation
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Production Systems
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procedural representation of knowledge
in the form of if – then rules (implication as the
primary representation element)
forward chaining control structure that operates
iteratively
inference mechanism is firing the rules
JESS: production system implemented with Java
4.3. Network Representation
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Semantic Networks
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particularly suited to model static world knowledge
world objects and classes of objects are modelled
as graph nodes
binary relations among them are captured as edges
between nodes
type of edge defines taxonomical relations between
nodes, i.e. subsumption of classes and object-class
membership
Source: http://zeus.csci.unt.edu/swigger/csci3210/
4.3. Network Representation
4.3. Network Representation
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Conceptual Graphs
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complete bipartite oriented graph, where each node
is either a concept or a relation between two
concepts
each concept has got its type and an instance
express meaning in a form that is logically precise,
humanly readable, and computationally tractable
Conceptual Graph Example
Source: http://www.jfsowa.com/cg/cgexampw.htm
4.3. Network Representation
4.4. Structural Representation
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Frames
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evolution of semantic networks
a hierarchy of frames
each frame has a:
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a name
slots: these are the properties of the entity that has the
name, and they have values (a default value, a specific
value, a deamon, an inherited value)
predecessor of object-oriented systems
Frames Example
Source:
ftp://ftp.cs.bham.ac.uk/pub/authors/M.Kerber/Teac
hing/AI/l6.pdf
4.4. Structural Representation
4.4. Structural Representation
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Scripts
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description of a class of events in terms of contexts,
participants, and sub-events
knowledge base representation in terms of the
situations that the system is supposed to
understand
restaurant script