Knowledge Representation - PowerPoint Presentation

Slide1

Knowledge RepresentationPart IIDescription Logic & Introduction to Protégé

Jan Pettersen Nytun

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The Semantic WebKnowledge Representation Part II, JPN, UiA2"The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“

Ref: "The Semantic Web" by 

Tim

Berners-Lee, James

Hendler

,

and

Ora

Lassila

,

Scientific

American, 2001Slide3

Linked Data/Semantic WebFrom Wikipedia…a method of publishing structured data so that it can be interlinked... …builds

upon standard Web technologies such as HTTP, RDF and

URIs…

it

extends them to share information in a way that can be

read automatically by

computers

.

This enables data from different sources to be connected and queried.

Knowledge Representation Part II, JPN, UiA

3Slide4

Some Semantic Web Technologies are Based on Description Logic (DL)DL is used in AI - modern ontology languages are based on description logics, e.g., OWL.Provide a logical formalism for ontologies and the Semantic Web. Much used in biomedical informatics codification of medical knowledge.Knowledge Representation Part II, JPN, UiA

4Slide5

Description Logic (DL) Continues…A description logic is used to describe classes, properties, and individuals. The knowledge base contains:Tbox (model): A terminological part which should remain constant as the domain being modelled changes.Abox (data): An assertional part

 describing what is true in some domain at some point in time.Knowledge Representation Part II, JPN, UiA

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Description Logic Continues… Terminology part (Tbox or Model):Defines concepts (also called classes), e.g., vital sign

, blood pressure, patient. Defines properties (also called

roles or property types),

e.g.,

hasBloodPressure

.

Knowledge Representation Part II, JPN, UiA

6Slide7

Description Logic Continues… Assertion part (ABox or Model Instance):Descriptions of individuals (also called objects) with their properties, e.g., description of a patient and the patients blood pressure.

Not all individuals in the assertion part may have been classified and this differs from ordinary object-oriented program development.

Knowledge Representation Part II, JPN, UiA

7Slide8

DL in ShortT-Box: Definition of Concepts (“Classes”), Roles (“Properties”) and Constraints.Subsumption Hierarchy (class-subclass hierarchies).A-Box: Assertions about individuals (instances) Unary predicates = concepts (e.g., Person, Boat) Binary predicates = roles Necessary

and Sufficient conditions on classes.Knowledge Representation Part II, JPN, UiA

8Slide9

User

Interface

Knowledge Base

Terminology (

TBox

) - Model

Assertions (

ABox

) - Model Instance

Classes

(Concepts)

Property

Types

Named

Individuals

Properties

Atomic

Complex

Asserted

Inferred

Classes

(Concepts)

Property

Types

Named

Individuals

Properties

Rules

Sensors

Sensor Handlers

Application

Software

Reasoner

Actuators

Actuator

Handlers

Query

EngineSlide10

10Protégé

A

free, open-source

OWL ontology editor and

framework

for building

intelligent systemsSlide11

11Protégé

Class hierarchy

(

Subsumption

hierarchy/taxonomy):

Patient

is subclass of Person which is subclass of

Thing.

Property hierarchy:

Properties are modeled separately

from Classes.

hasSSN

is sub property of

topDataProperty

. Slide12

12Property hasSSN has Person as domain.This means that an individual havingthis property must be of type Person,

i.e., it is an axiom stating that given anindividual with this property then it can be inferred that this individual is of typePerson

.

Protégé

Property hasSSN has string as

Range

.

I.e., the value of the property must

be a text string, e.g

., “17106575561

”.Slide13

Defining an Individual13

The type of the individual is “generic” (i.e., type is Thing).

Id is

janPN

(

complete

id:

http

://www.semanticweb.org/janpn/ontologies/2014/7/untitled-ontology-2#janPN

)

which we can assume is a globally unique id).

Individual has property hasSSN with value

“17106575561”.Slide14

Startingthe ReasonerSince janPN has property hasSSN then it must be a Person (i.e., the domain is Person for hasSSN)

.14

inferredSlide15

Type and Subclass as PropertiesType of an individual is stated as a property - .a property predefined in RDF called rdf:type. E.g.: ( Tom rdf:type Person )

Subclass is a property between classes. a property predefined in RDFS called rdfs:subClassOf. E.g.:

(

Employee

rdfs:subClassOf

Person

)

Knowledge Representation Part II, JPN, UiA

15Slide16

Knowledge BaseTerminology (TBox) - Model

Assertions (

ABox

) - Model Instance

Classes

(Concepts)

Property

Types

Named

Individuals

Properties

Atomic

Complex

Asserted

Inferred

Classes

(Concepts)

Property

Types

Named

Individuals

Properties

RulesSlide17

Complex ClassAn atomic class is somewhat like an “ordinary class”.A Complex class is built with

the help of description logic constructors

, properties

and

other classes (atomic or complex).

17Slide18

Complex Class Continues…Example using intersectionOf: Informally: A man is a human that is also a male

Formally: Class Man is the intersection of class Human and Male

In a more formal syntax:

EquivalentClass

(Man intersectionOf(Human Male))

18Slide19

19Example: Complex Class In Protégé

Reasoner infer that

Tom is a Man

(Alternatively you may specify that Man is subclass of Human and Man)

Run reasoner

AssertedSlide20

20Example: To be a parent you need to be human and additionally parent to at least one child.

Reasoner infers that Tom is a

HumanParent

Run reasoner Slide21

21To be a sick human you need to suffer from at least one sickness

Reasoner infers that Tom is a SickHuman

Tom and TomsDiabetes2

are individuals

Run reasoner Slide22

Knowledge Representation, Part II, JPN, UiA22

hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3)

Example of rule using The

Semantic Web Rule Language

 (

SWRL

):

Also SPARQL can be used

as a rule language.Slide23

ReferencesJan Pettersen Nytun, UiA, page 23[1] Book: David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010, http://artint.info/

[2] http://dsg.harvard.edu/courses/hst952/lecture12.ppt%E2%80%8E[3]

http://

www.jfsowa.com/logic/math.htm#Propositional

[4]

http://www.cs.ubc.ca/~kevinlb/teaching/cs322%20-%

202009-10/Lectures/Logic2.pdf

[5]

http://www.cs.ubc.ca/~kevinlb/teaching/cs322%20-%202009-10/Lectures/Logic1.pdf[6] http://artint.info/slides/ch05/lect2.pdf

Sowa, John F. (2000) Knowledge Representation: Logical, Philosophical, and Computational Foundations

, Brooks/Cole Publishing Co., Pacific Grove, CA.

Artificial

Intelligence: Structures and Strategies for Complex Problem Solving

(Addison-Wesley), George F. Luger

Smith

Barry. Accessed 24

th

of March, 2013,

Ontology: Philosophical and Computational.

http: //ontology.buffalo.edu/smith/articles/ontologies.htm

Quine

WVO.

On What There Is. Review of Metaphysics

1948;p. 21–38.