Sementic nets
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FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE
Riga Technical UniversityFaculty of Computer Science and Information Technology
Department of Systems Theory and Design
Dr.habil.sc.ing., professor Janis Grundspenkis, Dr.sc.ing., lecturer Alla Anohina-NaumecaDepartment of Systems Theory and DesignFaculty of Computer Science and Information TechnologyRiga Technical UniversityE-mail: {janis.grundspenkis, alla.anohina-naumeca}@rtu.lvAddress: Meza street 1/4- {550, 545}, Riga, Latvia, LV-1048 Phone: (+371) 67089{581, 595}
Lecture 7
KNOWLEDGE REPRESENTATION AND NETWORKED SCHEMES
Knowledge representation
• Knowledge representation is the method used to encode knowledge in an intelligent system’s knowledge base.
• The object of knowledge representation is to express knowledge in computer-tractable form, such that it can be used to help intelligent system perform well.
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Knowledge baseA knowledge base is an integral part of any knowledge-based intelligent
system. It maps objects and relationships of the real world to
computational objects and relationships.
Object 1 Object 2 Object 3Relation 1 Relation 2
Know ledge base
Domain
Object 1
Object 2Object 3
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Relation 1 Relation 2
But what is knowledge?• Knowledge is an abstract term that attempts to capture an
individual’s understanding of a given subject.
• In the world of intelligent systems the domain-specific knowledge is captured. Domain is a well-focused subject area.
• Cognitive psychologists have formed a number of theories to explain how humans solve problems. This work uncovered the types of knowledge humans commonly use, how they mentally organize this knowledge, and how they use it efficiently to solve a problem.
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Types of knowledge (1)
Declarative knowledge
ConceptsFactsObjects
Describes what is known about a problem. This includes simple statements that are asserted to be either true or false. This also includes a list ofstatements that more fully describes some object orconcept (object-attribute-value triplet).
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Procedural knowledge
RulesStrategiesAgendasProcedures
Describes how a problem is solved. This type of knowledge provides direction on how to do something.
Types of knowledge (2)
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HeuristicKnowledge
Rules of Thumb
Describes a rule-of-thumbthat guides the reasoning process. Heuristic knowledge is often called shallow knowledge. It is empirical and represents the knowledge compiled by an expert through the experience of solving past problems.
Types of knowledge (3)
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Meta-Knowledge
Knowledge about the other types of knowledge and how to use them
Describes knowledge about knowledge. This type of knowledge is used to pick other knowledge that is best suited for solving a problem. Experts use this type of knowledge to enhance the efficiency of problem solving by directing their reasoning in the most promising area.
Types of knowledge (4)
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StructuralKnowledge
Rule setsConcept relationshipsConcept to objectrelationships
Describes knowledge structures. This type of knowledge describes an expert’s overall mental model of the problem. The expert’s mental model of concepts, sub-concepts, and objects is typical of this type of knowledge.
Types of knowledge (5)
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Knowledge representation (1)
• In general, a representation is a set of conventions about how to describe a class of things.
• A description makes use of the conventions of a representation to describe some particular thing.
• The function of any representation scheme is to capture essential features of a problem domainand make that information available to a problem solving procedure.
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A representation consists of four fundamental parts:
• A lexical part that determines which symbols are allowed in the representation’s vocabulary.
• A structural part that describes constraints on how the symbols can be arranged.
• A procedural part that specifies access procedures that enable to create descriptions, to modify them, and to answer questions using them.
• A semantic part that establishes a way of associating meaning with the description.
Knowledge representation (2)
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Knowledge representation schemes (1)
• Logical schemes
− Predicate calculus
− Propositional calculus
• Procedural schemes
• Structured schemes− Scripts
− Frames
• Networked schemes− Semantic nets
− Conceptual graphs
− IF..THEN.. rules
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There are 4 schemes of knowledge representation:
Networked schemes use a graph to represent knowledge. Nodes of a
graph display objects or concepts in a domain, but arcs define
relationships between objects, their attributes and values of attributes.
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Structured schemes extend networked representation by displaying
each node in a graph as a complex data structure.
In procedural schemes knowledge is represented as a set of
instructions for problem-solving. That allows to modify a knowledge
base easily and to separate a knowledge base from an inference
mechanism.
Logical schemes represent knowledge, using mathematical or
orthographic symbols, inference rules and are based on precisely
defined syntax and semantics.
Knowledge representation schemes (2)
Semantic nets
Author: Quillian, 1967
Idea: Concepts are a part of knowledge about world. People perceive
concepts and reason with them. Concepts are related with relationships
between them. Relationships between concepts form understanding of
people.
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Definition of semantic netsSemantic network is a knowledge representation schema that captures
knowledge as a graph. The nodes denote objects or concepts, their
properties and corresponding values. The arcs denote relationshipsbetween the nodes. Both nodes and arcs are generally labelled (arcs
have weights).
Symbols of semantic nets:
Name
Name
- A concept
- A relationship
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Nodes of semantic nets can represent:
• Concepts
• Objects
• Events
• Features
• Time
• etc.
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Nodes of semantic nets
Relationships (1)Several kinds of relationships are used in semantic nets:
1. “Class - Superclass” or “IS-a” relationship
CarIs- a
Vehicle
Class Superclass
2. “Instance-class” or “Is an instance of” relationship
John’s carIs an instance of
Car
Instance Class
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Relationships (2)
3. “Part-Whole” or “Part of” relationship
DoorPart of
Car
Part Whole
4. “Object-Attribute” or “Has” relationship
John’s carHas
Color
Objects Attribute
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Relationships (3)
5. “Attribute-Value” or “Value” relationship
ColorValue
Red
Attribute Value
6. Logical relationships (and, or, not)
7. Linguistic relationships (examples: likes, owns, travels…)
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Inheritance (1)Inheritance is possible in semantic nets. Inheritance is a process by
which the local information of a superclass node is assumed by a
class node, a subclass node, and an instance node.
All vehicle have a brand nameand a model. A car is a class ofa superclass Vehicle. So Carinherits all features of Vehicle,that is, Brand Name and Model
Example:
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Vehicle
Model
Brand name
Car
has
Is a
has
Example of semantic nets
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owner
Is an instance of
Is a Vehicle
ValueValue
Has
Has
Value
John’s car
Car
Bank
works
Name Lateko
John Age 22
LA 657Reg.No.Brand name
ModelBMW
850Has
Has
Has
Value
Value
Conceptual graphs
Author: Sowa, 1984
A conceptual graph is a finite, connected, bipartite graph.
Two types of nodes are used in conceptual graphs:
- A concept
- A conceptual relationship
Name
Name
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Arcs of conceptual graphs (1)
In conceptual graphs the following arcs are allowed:
• Between a concept and a conceptual relationship
Name Name
• Between a conceptual relationship and a concept
NameName
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Arcs of conceptual graphs (2)
The following arcs are not allowed in conceptual graphs:
• Between a concept and a concept
Name
Name
• Between a conceptual relationship and a conceptual relationship
Name
Name
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Conceptual relationships (1)• Every conceptual relation r has a relation type t and a
nonnegative integer n called its valence.
• The number of arcs that belong to r is equal to its valence n. A conceptual relation of valence n is said to be n-adic, and its arcs are numbered from 1 to n.
• For every n-adic conceptual relation r, there is a sequence of nconcept types t1,...,tn, called the signature of r. A 0-adic conceptual relation has no arcs, and its signature is empty.
• All conceptual relations of the same relation type t have the same valence n and the same signature s.
• The term monadic is synonymous with 1-adic, dyadic with 2-adic, and triadic with 3-adic. 25/44
1-adic relation – Must be one outgoing arc from a conceptual relationship
NameName
2-adic relation – Must be one outgoing and one ingoing arc
3-adic relation – Must be two ingoing arcs and one outgoing arc
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Conceptual relationships (2)
NameNameName
NameNameName
Name
Concepts (1)
Concepts have the following form:
Concept = Type + Referent, where
Type is a type of a concept, cannot be empty;
Referent = Quantifier + Designator, can be empty
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Type: Referent
Teacher: MaryType Referent
1. A node containing only a type of a concept
Concepts (2)
Forms of cocnepts:
“There is a dog, but it is not specified which one dog”Dog
Type
2. Type + individual marker. Names of persons, places or
organizations can be displayed by an individual marker.
Dog: ReksiType Individual marker
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3. Specific but unnamed individual. Identity of a object can be
acquired from context performing inference
Concepts (3)
Dog: #134
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Cup: #
4. Several objects:- By listing them
- Using {*}
Birds: {*} Several birds
Guests: {John, Mary, Michael} Singagent object Song
5. Precise number of objects: @number
Concepts (4)
Person
6. Units of measurements
Interval: @18 sec
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Moves on Legs: @2
7. All by using “ or ∀
Fish: ∀ attribute wet
All fish are wet
8. A conceptual graph can include a concept which is a conceptual
graph by itself
Concepts (5)
believes
agent
object
experiencer
Person: Jane likes
Person: Tom
objectpizza
Example:
proposition
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9. Different combinations
Concepts (6)
Number: 18
Number: @18 18
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Number: @18
There is a number 18
Number: {*} @5 18
There are eighteen numbers
There are eighteen numbers and all ofthem are equal with 18
There are 5 numbers and all are equalwith 18
Operations of conceptual graphs (1)
Theory of conceptual graphs defines 4 operations:
• Copying
• Restricting
• Joining
• Simplifying
Copying allows acquiring of a new conceptual graph G1 which is
identical with the already existent conceptual graph G.
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Restricting allows replacing of a concept node by its specialization.
Two cases are possible:
• Type can be replaced by an individual marker
• Type can be replaced by its subtype
Joining allows joining of two conceptual graphs if they have an
identical concept node.
Simplifying allows removing of one of two identical nodes of a
conceptual relation together with all its arcs.
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Operations of conceptual graphs (2)
In order to apply the mentioned operations a type hierarchy must be
defined: if s and t are types of concepts and t≤s, then t is subtype of s.
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Examples:Manager ≤ Employee ≤ Person
Dog ≤ Animal
John ≤ Man ≤ Person
Operations of conceptual graphs (3)
Example:For example, we have two conceptual graphs G1 and G2 and a type hierarchyDog ≤ Animal
brown
Is a
colorAnimal
Meat-eater
brown
location
colorDog: Reksi
porchG2
G1
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Operations of conceptual graphs (4)
Example:Restricting operation can be applied to the graph G1 by replacing type Animalwith its subtype Dog: Reksi. A new graph G3 is acquired as a result.
G3
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brown
Is a
colorDog: Reksi
Meat-eater
Operations of conceptual graphs (5)
Example:Now we can join graphs G2 un G3, because they have an identical concept nodeDog:Reksi. A new graph G4 is acquired.
brown
Is a
color
Meat-eater
brown
location
colorDog:Reksi
porchG4
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Operations of conceptual graphs (6)
Example:By simplifying the graph G4 a new graph G5 is acquired.
Is a Meat-eater
brown
location
colorDog:Reksi
porchG5
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Operations of conceptual graphs (7)
Inheritance in conceptual graphs
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By using restriction and joining operations of conceptual graphs it is possible to
support inheritance. When a type is replaced by an individual marker an
instance inherits features from a type. When a type is replaced by a subtype
then the subtype inherits features from the type.
Part ofPrimate hand
Part ofChimpanzee hand
Part ofChimpanzee: bonzo hand
Example:
Inheritance made by a subtype
Inheritance made by an instance
Type
Subtype
An individual marker
replaces
replaces
The type hierarchy Chimpanzee ≤ Primate is defined
Logic and conceptual graphs (1)
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In conceptual graphs it is possible to represent logical operations AND,
OR and NOT.
1. Negation is implemented using a propositional node and a unary
conceptual relation NOT
agent
NOT
Shine Sun
proposition
Example:A conceptual graph displaying a sentence “The sun is not shining”
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2. Conjunction is displayed by placing both conceptual graphs in
the common propositional node.
attributeStudy course Interesting
proposition
Example:A conceptual graph displaying a sentence “The study course is interesting anddifficult”
attribute DifficultStudy course
Logic and conceptual graphs (2)
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Disjunction is represented by negation and conjunction:
1. A graph G1 must be placed an a propositional node and its negation must
be made
2. A graph G2 must be placed an a propositional node and its negation must
be made
3. Both negations must be placed in a propositional node and its negation
must be made
attributePerson: John silly
proposition
Example:
attribute smart
proposition
Not
proposition
Person: JohnNot
Not
Logic and conceptual graphs (3)
mean
Example
Student: # John
Language: C# language Program
Student: #
Company: # Applications
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Student: #agent
Developagent object
Work Company: #
Name
mean
agentplace
G1
G2
G4
G5
Company: # EuroSoftNameG3
Language: C# language