sementic nets

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.


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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.


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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.


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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.


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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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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


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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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


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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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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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


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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


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