Semantic Web: State of the Art and Opportunities Vagan Terziyan Compiled, partly based on various online tutorials and presentations, with respect to their

Semantic Web:Semantic Web:State of the Art and OpportunitiesState of the Art and Opportunities

Vagan TerziyanCompiled, partly based on various online tutorials

and presentations, with respect to their authors

“Industrial Ontologies” Group

http://www.cs.jyu.fi/ai/OntoGroup/index.html

University of Jyväskylä

Industrial Ontologies GroupIndustrial Ontologies Group

4

Web Limitations

Doubles in sizeevery six months

Average WWW searches examineonly about 25% of potentially

relevant sites and return a lot ofunwanted information

Information on web is not suitablefor software agents

World Wide Web

Semantic Web

The Semantic Web is avision: the idea of havingdata on the Web defined andlinked in a way that it can beused by machines not just fordisplay purposes, but forautomation, integration andreuse of data across variousapplications.

7

B e f o r e S e m a n t i c W e b

W e b c o n t e n t

U s e r sC r e a t o r sW W Wa n dB e y o n d

8

S e m a n tic W e b S tru c tu re

S e m a n ticA n n o ta tio n s

O n to lo g ie s L o g ic a l S u p p o rt

L a n g u a g e s T o o ls A p p lic a tio n s /S e rv ic e s

W e b c o n te n t

U se rsC re a to rsW W Wa n dB e y o n d

S e m a n ticW e b

Motivation for Semantic Web Motivation for Semantic Web

Limitations of the Web today

Machine-to-human, not machine-to-machine

Summarizing the Problem: Computers don’t understand Meaning

• “My mouse is broken. I need a new one…”

Use of ontology

“My mouse is broken” vs. “My mouse is dead”

Approach: Semantic WebApproach: Semantic Web

“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes,

but for automation, integration and reuse

of data across various applications”

http://www.w3.org/sw/

The Semantic Web is an initiative with the goal of extending the current Web and facilitating Web automation, universally accessible web resources, and the 'Web of Trust', providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people.

Tim Berners-Lee's Vision of Semantic Web (IJCAI-01)

Semantic Web Stack(updated, W3C, 2006)

Semantic Web: New “Users” Semantic Web: New “Users”

SemanticAnnotations

Ontologies Logical Support

Languages Tools Applications /Services

Web content

UsersCreatorsWWWandBeyond

SemanticWeb

Semantic Webcontent

UsersSemanticWeb andBeyond

Creators

applications

agents

Semantic Web: Annotations

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

Semantic annotations are specific sort of metadata, which provides information about particular domain objects, values of their properties and relationships, in a machine-processable, formal and standardized way.

Semantic Web: Ontologies

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

Ontologies make metadata interoperable and ready for efficient sharing and reuse. It provides shared and common understanding of a domain, that can be used both by people and machines. Ontologies are used as a form of agreement-based knowledge representation about the world or some part of it and generally describe: domain individuals, classes, attributes, relations and events.

Semantic Web: Rules

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

Logical support in form of rules is needed to infer implicit content, metadata and ontologies from the explicit ones. Rules are considered to be a major issue in the further development of the semantic web. On one hand, they can be used in ontology languages, in conjunction with or as an alternative to description logics. And on the other hand, they will act as a means to draw inferences, to configure systems, to express constraints, to specify policies, to react to events/changes, to transform data, to specify behavior of agents, etc.

Semantic Web: Languages

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

Languages are needed for machine-processable formal descriptions of: metadata (annotations) like e.g. RDF; ontologies like e.g. OWL.; rules like e.g. RuleML. The challenge is to provide a framework for specifying the syntax (e.g. XML) and semantics of all of these languages in a uniform and coherent way. The strategy is to translate the various languages into a common 'base' language (e.g. CL or Lbase) providing them with a single coherent model theory.

Semantic Web: Tools

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

User-friendly tools are needed for metadata manual creation (annotating content) or automated generation, for ontology engineering and validation, for knowledge acquisition (rules), for languages parsing and processing, etc.

Semantic Web: Applications and Services

SemanticAnnotations



Web content


SemanticWeb

Semantic Webcontent


Creators

applications

agents

Utilization of Semantic Web metadata, ontologies, rules, languages and tools enables to provide scalable Web applications and Web services for consumers and enterprises" making the web 'smarter' for people and machines.

The Semantic Web

The Ontology Articulation Toolkit helps agents to

understand unknown ontologies

Semantic Web basics…

RDF:

• is a W3C standard, which provides tool to describe Web resources

• provides interoperability between applications that exchange machine-understandable information

RDF Schema:– is a W3C standard which defines vocabulary for RDF– organizes this vocabulary in a typed hierarchy – capable to explicitly declare semantic relations between

vocabulary terms

Where we are Today: the Syntactic Web

[Hendler & Miller 02]

Most of the Current Web (dumb links) Most of the Current Web (dumb links)

Semantic WebSemantic Web(data connected by relationships)(data connected by relationships)

Mary

Director

Secretary

to_be_in_love_with

has_job

has_job

John

has_homepage

has_homepage

OntologyOntology

RDF – Semantic Web over Web Resources

Resources

• All things being described by RDF expressions are called resources:– entire Web page;– a specific XML element;– whole collection of pages;– an object that is not directly accessible via the

Web.

Semantic Predicate

otherwise 0,

; Aobject withL named relation

by connected is Aobject if 1,

=)A,L,P(A jk

i

jki

AiAj

Lk

Ai

Lk

Relation (i j)

Property (i = j)

46

Semantic Function

)A,(AL jik

AiAj

Lk

Ai

Lk

Relation

Property

C),(AL ik

Name of Function Variables

Value of property

47

Different Ways to Represent properties

RED Tomato has_color

Tomato

has_red_color

Tomato

instance_of

Red Thing

C

in RDF

in RDFSin semantic network

48

RDF StatementRDF Statement

Resource_i Value_nProperty_k

Resource_iProperty_r

Resource_ j

OR

49

Semantic Relation as RDF statement(so called “object property”)

Ai Aj

Lk

Relation (i j)

http://www.cs.jyu.fi/ai/vagan/index.html http://www.jyu.fi/agora-center/indexEng.htmlPersonal web page of Terziyan V. Web page of Agora Center

refers_to

ResourceRelation

Resource

Subject objectPredicate

http://www.cs.jyu.fi/ai/vagan/#vaganURI of Terziyan V. employed_by

Dereferenceable URI (“Hash vs. Slash”)

URI of Agora Center http://www.jyu.fi/agora-center/#AC

Dereferenceable URI (“Hash vs. Slash”)

50

Semantic Property as RDF statement (so called “datatype property”)

http://www.cs.jyu.fi/ai/vagan/index.htmlPersonal web page of Terziyan V.

Literal

has_birthday

ResourceProperty

Subject objectPredicate

LiteralAi

LkProperty (i = j)

15.02.2000

http://www.cs.jyu.fi/ai/vagan/#vaganURI of Terziyan V. has_birthday

27.12.1958Dereferenceable URI (“Hash vs. Slash”)

“Birthday” of the web-page

Birthday of Terziyan V.

51

Semantic Network of Web Resources

http://www.cs.jyu.fi/ai/vagan/index.html http://www.jyu.fi/agora-center/indexEng.htmlPersonal web page of Terziyan V. Web page of Agora Center

refers_to

http://www.cs.jyu.fi/ai/vagan/#vaganURI of Terziyan V. employed_by URI of Agora Center

http://www.jyu.fi/agora-center/#AC

hasWebPage

isWebPageOf

hasWebPage

isWebPageOf27.12.1958

has_birthday

52

From Hyperlinks to Semantic Web

http://www.cs.jyu.fi/ai/ university

http://www.kture.kharkov.ua/

international_contacts

http://www.cs.jyu.fi/ai/contacts.html

Resources and URIs

• A resource can be anything that has identity

• Uniform Resource Identifiers (URI)* provide a simple and extensible means for identifying a resource

• Not all resources are network "retrievable"; e.g., human beings, corporations, and books in a library can also be considered resources

* The term "Uniform Resource Locator" (URL) refers to the subset of URI that identify resources via a representation of their primary access mechanism (e.g., their network "location"), rather than identifying the resource by name or by some other attribute(s) of that resource.

URI

Venn diagram of Uniform Resource Identifier (URI) scheme categories. Schemes in the URL (locator) and URN (name) categories both function as resource IDs, so URL and URN are subsets of URI. They are also, generally, disjoint sets. However, many schemes can't be categorized as strictly one or the other, because all URIs can be treated as names, and some schemes embody aspects of both categories – or neither.

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

Dereferenceable URIDereferenceable URIThe term Linked Data is used to describe a method of exposing, sharing, and connecting data via dereferenceable* URIs on the Web. Linked Data is about using the Web to connect related data that wasn’t previously linked, or using the Web to lower the barriers to linking data currently linked using other methods. More specifically, Wikipedia defines Linked Data as a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF. Linked Data aims to extend the Web with a data commons by publishing various open datasets as RDF on the Web and by setting RDF links between data items from different data sources.

*A dereferenceable Uniform Resource Identifier or dereferenceable URI is a resource retrieval mechanism that uses any of the internet protocols (e.g. HTTP) to obtain a copy or representation of the resource it identifies. In the context of traditional HTML web pages, this is the normal and obvious way of working: A URI refers to the page, and when requested the web server returns a copy of it. In other non-dereferenceable contexts, such as XML Schema, the namespace identifier is still a URI, but this is simply an identifier (i.e. a namespace name). There is no intention that this can or should be dereferenced. There is even a separate attribute, schemaLocation, which may contain a dereferenceable URI that does point to a copy of the schema document. In the case of Linked Data, the representation takes the form of a document (typically HTML or XML) that describes the resource that the URI identifies. In either case, the mechanism makes it possible for a user (or software agent) to "follow your nose" to find out more information related to the identified resource.

• Subject of an RDF statement is a resource

• Predicate of an RDF statement is a property of a resource

• Object of an RDF statement is the value of a property of a resource

RDF Statement

Example of RDF Statement

Subject (resource) http://www.w3.org/Home/Lassila

Predicate (property) Creator

Object (literal) “Ora Lassila”

Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila.

RDF Example (subject of statement)


<rdf:RDF> <rdf:Description about= "http://www.w3.org/Home/Lassila"> <s:Creator>Ora Lassila</s:Creator> </rdf:Description></rdf:RDF>

Subject

RDF Example (predicate of statement)



Predicate

RDF Example (object of statement)



Object

RDF Example (reference to ontology)


<rdf:RDF> <rdf:Description about= "http://www.w3.org/Home/Lassila"> <s:Creator>Ora Lassila</s:Creator> </rdf:Description></rdf:RDF> a specific namespace prefix as reference to

ontology where predicates are defined, e.g. xmlns: s="http://description.org/schema/"

Full XML Document for the Example

<?xml version="1.0"?><rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#”

xmlns:s="http://description.org/schema/"> <rdf:Description about= "http://www.w3.org/Home/Lassila"> <s:Creator>Ora Lassila</s:Creator> </rdf:Description></rdf:RDF>

Namespaces as attributes of “RDF” element in XML

RDF Abbreviated Syntax

• While the serialisation syntax shows the structure of an RDF model most clearly, often it is desirable to use a more compact XML form.

• The RDF abbreviated syntax accomplishes this.

Abbreviated Syntax Example (1)

<rdf:RDF> <rdf:Description about="http://www.w3.org/Home/Lassila" s:Creator="Ora Lassila" /></rdf:RDF>



<rdf:Description about="http://www.w3.org/Home/Lassila" s:Creator="Ora Lassila" />

Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila. Subject


<rdf:Description about="http://www.w3.org/Home/Lassila" s:Creator ="Ora Lassila" />


Predicate


<rdf:Description about="http://www.w3.org/Home/Lassila" s:Creator="Ora Lassila" />


Object

RDF N3 syntax

• Notation3, or N3 as it is more commonly known, is a shorthand non-XML serialization of RDF models, designed with human-readability in mind: N3 is much more compact and readable than XML RDF notation. The format is being developed by Tim Berners-Lee and others from the Semantic Web community.

RDF sample in XML notation

RDF sample in N3 notation

RDF N3 examples

• Simple statement :John :Loves :Mary

• Reified statement{:John :Loves :Mary} :accordingTo :Bill

• Goal statement:gb:I gb:want {:John :Loves :Mary}

The prefix gb: is used here to denote the ontology of S-APL.

Some N3 syntax specifics

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>

:Professor a rdfs:Class

<http://www.cs.jyu.fi/ai/vagan> a :Professor

Ontological statements in N3

Statements about Statements (1)

An unnamed node is the source of all five arcs. The first arc is labelled rdf:type and points to the node identified as rdf:Statement. The second arc is labelled rdf:predicateand points to the node identified as s:Creator. The third arc is labelled rdf:subject and points to a node labelled http://www.w3.org/Home/Lassila. The fourth arc is labelled rdf:object and points to a node containing the string value "Ora Lassila". The fifth and final arc is labelled a:attributedTo and points to a node containing the string value "Ralph Swick".

“Ralph Swick says that Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila”

Statements about Statements (2)

“Ralph Swick says that Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila”

<rdf:RDF xmlns:rdf="http://w3.org/TR/1999/PR-rdf-syntax-19990105#" xmlns:a="http://description.org/schema/"> <rdf:Description> <rdf:subject resource="http://www.w3.org/Home/Lassila" /> <rdf:predicate resource="http://description.org/schema#Creator" /> <rdf:object>Ora Lassila</rdf:object> <rdf:type resource="http://w3.org/TR/1999/PR-rdf-syntax- 19990105#Statement" /> <a:attributedTo>Ralph Swick</a:attributedTo> </rdf:Description> </rdf:RDF>

What is RDFS ?

• RDF Schema – Defines vocabulary for RDF– Organizes this vocabulary in a typed hierarchy

(Class, subClassOf, type, Property, subPropertyOf)• Rich, web-based publication format for declaring

semantics (XML for exchange)• Capability to explicitly declare semantic relations

between vocabulary terms

RDF Schema

• Semantic network on the Web

• Nodes are identified by URIs

• rdfs:Class

• rdfs:Property

• rdfs:subClassOf

106

Dublin Core

• A set of fifteen basic properties for describing generalised Web resources

• ISO Standard 15836-2003 (February 2003): http://www.niso.org/international/SC4/n515.pdf

http://dublincore.org/

The Dublin Core Metadata Initiative is an open forum engaged in the development of interoperable

online metadata standards that support a broad range of purposes and business models.

107

Dublin Core (15 basic properties):

• Title

• Creator

• Subject

• Description

• Publisher

• Contributor

• Date

• Type

• Format

• Identifier

• Source

• Language

• Relation

• Coverage

• Rights

109

Where to look next

• RDF:http://www.w3.org/RDF/

• RDF Schema:http://www.w3.org/TR/rdf-schema/

Traditional RDF StatementTraditional RDF Statement

Resource_i LiteralProperty_k

Resource_iProperty_r

Resource_ j

OR

• Subject of an RDF statement is a resource• Predicate of an RDF statement is a property of a

resource• Object of an RDF statement is the value of a property

of a resource (either literal or resource)

New semantics of RDF Statement in S-APL (object - executable resource)New semantics of RDF Statement in S-APL (object - executable resource)

Resource_iProperty_m exe: Resource_

j

executable resourceSemantics of such statement means that the value of the

Property_m of the Resource_i can be obtained as a result of

execution of the procedure (query, service, function, etc.) represented

as Resource_ j

S-APLS-APLSemantic Agent Programming Language

(Designed by Industrial Ontologies Group)

Ontological Vision of Semantic Web

Semantic Web needs ontologies

An ontology is document or file that formally and in a

standardized way defines the hierarchy of classes within the domain, semantic relations among terms and inference rules

Use of ontologies: Sharing semantics of your data across

distributed applications

Communication between people

Studer(98): Formal, explicit specification of a shared conceptualization

Machine readable

Concepts, properties,functions, axiomsare explicitly defined

Consensualknowledge

Abstract model of some phenomenain the world

What is an ontology?

121

What is an Ontology?From: Ian Horrocks “OWL 2: The Next Generation”

122

What is an Ontology?A model of (some aspect of) the world

From: Ian Horrocks “OWL 2: The Next Generation”

123


• Introduces vocabulary relevant to domain, e.g.:

– Anatomy


124



– Anatomy

– Cellular biology


125



– Anatomy


– Aerospace


126



– Anatomy


– Aerospace

– Dogs


127



– Anatomy


– Aerospace

– Dogs

– Hotdogs

– …


128


• Introduces vocabulary relevant to domain

• Specifies meaning of terms

Heart is a muscular organ thatis part of the circulatory system


129


• Introduces vocabulary relevant to domain

• Specifies meaning of terms

Heart is a muscular organ thatis part of the circulatory system

• Formalised using suitable logic


130

DL SemanticsSemantics given by standard FO model theory:

Interpretation domain IInterpretation function I

Individuals iI 2 I

John

Mary

Concepts CI µ I

Lawyer

Doctor

Vehicle

Roles rI µ I £ I

hasChild

owns

(Lawyer u Doctor)

From: Ian Horrocks “OWL: A Description Logic Based Ontology Language”

Ontology Elements

•Concepts(classes) + their hierarchy

•Concept properties (slots/attributes)

•Property restrictions (type, cardinality, domain)

•Relations between concepts (disjoint, equality)

•Instances

How to build an ontology?

Steps:

•determine domain and scope

•enumerate important terms

•define classes and class hierarchies

•define slots

•define slot restrictions (cardinality, value-type)

Step 1: Determine Domain and Scope

Domain: geography

Application: route planning agent

Possible questions: Distance between two cities?

What sort of connections exist between two cities?In which country is a city?How many borders are crossed?

Step 2: Enumerate Important Terms

country

city capital

border

connection

Connection_on_land

Connection_in_air

Connection_on_water

road

railway

currency

Step 3: Define Classes and Class Hierarchy

Step 4: Define Slots of Classes

Step 5: Define slot constraints

•Slot-cardinalityEx: Borders_with multiple, Start_point single

•Slot-value typeEx: Borders_with- Country

Geographic_entity

Country CityHas_capital

Capital_ofBorders_with

ConnectionStart_point

End_point

Capital_city

OWL became standard

• 10 February 2004 the World Wide Web Consortium announced final approval of two key Semantic Web technologies, the revised Resource Description Framework (RDF) and the Web Ontology Language (OWL).

• Read more in:

http://www.w3.org/2004/01/sws-pressrelease.html.en

• What is OWL? – OWL is a language for defining Web

Ontologies and their associated Knowledge Bases

– The OWL language is a revision of the DAML+OIL web ontology language incorporating learning from the design and application use of DAML+OIL.

OWL IntroductionOWL Introduction

Example

• There are two types of animals, Male and Female.

<rdfs:Class rdf:ID="Male"> <rdfs:subClassOf rdf:resource="#Animal"/></rdfs:Class>

• The subClassOf element asserts that its subject - Male - is a subclass of its object -- the resource identified by #Animal.

<rdfs:Class rdf:ID="Female"> <rdfs:subClassOf rdf:resource="#Animal"/> <owl:disjointWith rdf:resource="#Male"/></rdfs:Class>

• Some animals are Female, too, but nothing can be both Male and Female (in this ontology) because these two classes are disjoint (using the disjointWith tag).

OWL Example in Protégé (1)

• Class– Person superclass– Man, Woman subclasses

• Properties– isWifeOf, isHusbandOf

• Property characteristics, restrictions– inverseOf– domain– range– Cardinality

• Class expressions– disjointWith



• Symmetric: if P(x, y) then P(y, x)• Transitive: if P(x,y) and P(y,z) then P(x, z)• Functional: if P(x,y) and P(x,z) then y=z• InverseOf: if P1(x,y) then P2(y,x)• InverseFunctional: if P(y,x) and P(z,x) then y=z• allValuesFrom: P(x,y) and y=allValuesFrom(C)• someValuesFrom: P(x,y) and y=someValuesFrom(C)• hasValue: P(x,y) and y=hasValue(v)• cardinality: cardinality(P) = N• minCardinality: minCardinality(P) = N• maxCardinality: maxCardinality(P) = N• equivalentProperty: P1 = P2• intersectionOf: C = intersectionOf(C1, C2, …)• unionOf: C = unionOf(C1, C2, …)• complementOf: C = complementOf(C1)• oneOf: C = one of(v1, v2, …)• equivalentClass: C1 = C2• disjointWith: C1 != C2• sameIndividualAs: I1 = I2• differentFrom: I1 != I2• AllDifferent: I1 != I2, I1 != I3, I2 != I3, …• Thing: I1, I2, …

OWL on one Slide

Legend:Properties are indicated by: P, P1, P2, etcSpecific classes are indicated by: x, y, zGeneric classes are indicated by: C, C1, C2Values are indicated by: v, v1, v2Instance documents are indicated by: I1, I2, I3, etc.A number is indicated by: NP(x,y) is read as: “property P relates x to y”

An Example

• Woman ≡ Person ⊓ Female• Man ≡ Person ⊓ Woman• Mother ≡ Woman ⊓ hasChild.Person• Father ≡ Man ⊓ hasChild.Person• Parent ≡ Father ⊔ Mother• Grandmother ≡ Mother ⊓ hasChild.Parent

We can further infer (though not explicitly stated):

Grandmother Person⊑ Grandmother Man ⊑ ⊔ Woman

etc.

• W3C Documents– Guide: http://www.w3.org/TR/owl-guide/– Reference: http://www.w3.org/TR/owl-ref/– Semantics and Abstract Syntax:

http://www.w3.org/TR/owl-semantics/• OWL Tutorials

– Ian Horrocks, Sean Bechhofer:http://www.cs.man.ac.uk/~horrocks/Slides/Innsbruck-tutorial/

– Roger L. Costello, David B. Jacobs: http://www.xfront.com/owl/

• Example Ontologies, e.g. here:http://www.daml.org/ontologies/

Resources

Tutorial: Designing Ontologies with Protégé

http://www.cs.man.ac.uk/~horrocks/Teaching/cs646/

http://www.co-ode.org/resources/tutorials/ProtegeOWLTutorial.pdf

• Protégé is an ontology editor and a knowledge-base editor (download from:

http://protege.stanford.edu ).• Protégé is also an open-source,

Java tool that provides an extensible architecture for the creation of customized knowledge-based applications.

• Protégé's OWL Plug-in now provides support for editing Semantic Web ontologies.

PLEASE !!!Download version:Protégé 3.4.4.

http://www.cs.jyu.fi/ai/vagan/Ontologies.ppt

Web of Trust

• Claims can be verified if there is supporting evidence from another (trusted) source– We only believe that someone is a professor at a

university if the university also claims that person is a professor, and the university is on a list I trust.

believe(c1) :- claims(x, c1) ^ predicate(c1, professorAt) ^ arg1(c1, x) ^ arg2(c1, y) ^ claims(c2, y) ^ predicate(c2, professorAt) ^ arg1(c2, x) ^ arg2(c2, y) ^ AccreditedUniversity(y)

AcknowledgedUniversity(u) :- link-from(“http://www.cs.umd.edu/university-list”,u)

Notice this one

Rules on top of Semantic Web (“Metasemantics”)

Ì Å Ò À Ñ Å Ì À Í Ò È Ê À

Ïðîäóêöèîííûå

ïðàâèëà ïðàâèëàÂðåìåííûå

ïðàâèëàÑåìàíòè÷åñêèå

Ñåìàíòè÷åñêàÿ

ñåòü1

2

3

5

410

11 89

6

7

R T

S

P

M

Production Rules

Temporal Rules

Semantic Rules

Semantic Web

Metasemantics

State of a Semantic Net

)A,L,(AP)A,L,(AP=S(t) qnmt

0=)qA,nL,m(AtPqn,m,

jkit

1=)jA,kL,i(AtPkj,i,

)A,L,P(A)A,L,P(A=S(t) 221211 )A,L,P(A)A,L,P(A 342232

A1 A

A2

3

L1

L

L

L

2

3

4

P=P(A,L,A)1 11 2 P=P(A,L,A)2 12 2

P=P(A,L,A)3 2 3 2 P=P(A,L,A)4 2 4 3

4321 PPPP=S(t)

Production Rules

kii P THEN )IF(S :R

kjj P THEN )IF(S :R

iki SP=S

jkj SP=S

.P THEN ))PP(PP( IF :R 243121

A1 A

A2

3

L1

L

L

3

4

A1 A

A2

3

L1

L

L

L

2

3

4

,*)ii LP(*,=P

Production-Based Reasoning

Initial state

State after ntransformations

Set of Production Rules

S(t0) S(t0+n·τ)

Example (not formalised rules)

1. Mary will love John if he loves her and if he is not abusing Pete.

2. Pete will consider Mary as his friend if she is not in love with John.

3. Pete will not consider Mary as his friend if she is in love with John who is abusing him.

4. John will stop loving Mary if she does not love him or she is a friend of Pete.

5. Mary will stop loving John if he is abusing Pete.

6. John being in bad mood will abuse Pete.

7. John gets rid of bad mood if Mary loves him or if she is not a friend of Pete.

8. John will fall in a bad mood if he loves Mary and she does not love him or vice versa.

9. John will stop abusing Pete if he (John) does not love Mary any more or if she is not a friend of Pete.

Example (formalised rules)

R : IF ( P P P ) THEN P1 1 4 2 2 ;R : IF ( P P ) THEN P2 2 3 3 ;R : IF ( P P P ) THEN P3 2 4 3 3 ;R : IF ( ( P P ) P ) THEN P4 3 2 1 1 ;R : IF ( P P ) THEN P5 2 4 2 ;R : IF ( P P ) THEN P6 5 4 4 ;R : IF ( ( P P ) P ) THEN P7 2 3 5 5 ;R : IF ( ( P P P P ) P ) THEN P8 1 2 1 2 5 5 ;R : IF ( ( P P ) P ) THEN P9 1 3 4 4 .

1. Mary will love John if he loves her and if he is not abusing Pete.

2. Pete will consider Mary as his friend if she is not in love with John.

3. Pete will not consider Mary as his friend if she is in love with John who is abusing him.

4. John will stop loving Mary if she does not love him or she is a friend of Pete.

5. Mary will stop loving John if he is abusing Pete.

6. John being in bad mood will abuse Pete.

7. John gets rid of bad mood if Mary loves him or if she is not a friend of Pete.

8. John will fall in a bad mood if he loves Mary and she does not love him or vice versa.

9. John will stop abusing Pete if he (John) does not love Mary any more or if she is not a friend of Pete.

P1 = P(A1, L1, A2) - John loves Mary;

P2 = P(A2, L1, A1) - Mary loves John;

P3 = P(A3, L2, A2) - Pete has a friend Mary;

P4 = P(A1, L3, A3) - John is abusing Pete;

P5 = P(A1, L4, A1) - John has a bad mood.

Example (reasoning, 1-st step)

John Mary

Pete

to love

to have a friend

543210 PPPPP=)S(t

R : IF ( P P P ) THEN P1 1 4 2 2 ;R : IF ( P P ) THEN P2 2 3 3 ;R : IF ( P P P ) THEN P3 2 4 3 3 ;R : IF ( ( P P ) P ) THEN P4 3 2 1 1 ;R : IF ( P P ) THEN P5 2 4 2 ;R : IF ( P P ) THEN P6 5 4 4 ;R : IF ( ( P P ) P ) THEN P7 2 3 5 5 ;R : IF ( ( P P P P ) P ) THEN P8 1 2 1 2 5 5 ;R : IF ( ( P P ) P ) THEN P9 1 3 4 4 .543210 PPPPP=)S(t

Example (reasoning, 2-nd step)

John Mary

Pete

to love

to have a friend


543210 PPPPP=)S(t

543210 PPPPP=)S(t 2

to have a bad mood

Example (reasoning, 3-rd step)

John Mary

Pete

to love

to have a friend


543210 PPPPP=)S(t 2

to abuse

543210 PPPPP=)S(t 3

Example (reasoning, 4-th step)

John Mary

PeteR : IF ( P P P ) THEN P1 1 4 2 2 ;R : IF ( P P ) THEN P2 2 3 3 ;R : IF ( P P P ) THEN P3 2 4 3 3 ;R : IF ( ( P P ) P ) THEN P4 3 2 1 1 ;R : IF ( P P ) THEN P5 2 4 2 ;R : IF ( P P ) THEN P6 5 4 4 ;R : IF ( ( P P ) P ) THEN P7 2 3 5 5 ;R : IF ( ( P P P P ) P ) THEN P8 1 2 1 2 5 5 ;R : IF ( ( P P ) P ) THEN P9 1 3 4 4 .

543210 PPPPP=)S(t 3

to have a bad mood

543210 PPPPP=)S(t 4

Example (reasoning, 5-th step)

John Mary


543210 PPPPP=)S(t 4

543210 PPPPP=)S(t 5

to abuse to have a friend

Example (reasoning, reaching terminal state)

John Mary


543210 PPPPP=)S(t 5

to have a friend

terminal state

Example (final “terminal” state)

1. John does not love Mary.

2. Mary does not love John.

3. Mary is a friend of Pete.

4. John is not abusing Pete.

5. John is not in a bad mood.

543210 PPPPP=)5S(t

Example (another initial and in the same time terminal state)

1. John loves Mary.

2. Mary loves John.

3. Mary is not a friend of Pete.

4. John is not abusing Pete.

5. John is not in a bad mood.

543210 PPPPP=)S(t

John Mary

Pete

to love

to love


terminal state

Example (two possible terminal states of the “love triangle”)

John Mary

Pete

to love

to love

John Mary

Peteto have a friend

Example (asynchronous reasoning tree)

54321 PPPPP


54321 PPPPP 54321 PPPPP 54321 PPPPP

R1R4

R8

54321 PPPPP

R4

R1

54321 PPPPP

R6

54321 PPPPP 54321 PPPPP

R4

54321 PPPPP

R8R4 R6

54321 PPPPP

R4

54321 PPPPP

R6

54321 PPPPP R9

54321 PPPPP R5

R3

R7

54321 PPPPP 54321 PPPPP 54321 PPPPP

R5R9 R7

R3

54321 PPPPP

R5

R8R9

R2

54321 PPPPP

R7

54321 PPPPP R9

54321 PPPPP

R6 R7R5

R8

R9

R9

54321 PPPPP

R9 R2R2

R6

R7

R9

R2

R8

Temporal Rules

• Lifetime of a relation

• Restoration time of a relation

Lifetime of a Relation

T A A i j L k

Lifetime of relation Lk, which means

that since appearance in the network this relation is valid T units of time

Example (Initial state of the network)

1

4

2

4

3

A 2 L 2

L 3

L 4

L 5

L 1

L 6

A 1 A 3

t = t0

Example (Network evolution)

A 1 A 1

A 2 A 2

A 3 A 3

L 2 L 2

L 3

L 4 L 4

L 5 L 5

L 6 L 6

A 1 A 1

A 2 A 2

A 3

L 2

L

L 4 L 4

L 5

6

A 3

t = t0 + τ t = t0 + 2τ

t = t0 + 3τ t = t0 + 4τ

Restoration Time of a Relation

A i A j L k

T ~

Restoration (“relaxation”) time of

relation Lk, which means that since

removal from the network this relation will be restored and become valid again after T units of time

~

Example

2

A 2

L 3

L 4

L

L

A 1 A 3

1

2

1

3

2

Example (Network evolution)

A 1 A 1

A 2 A 2

A 3 A 3 L 4

A 1

A 2

L A 3

4

L 3

L 1

A 1

A 2

L A 3

4

L 3

L 1

L 2

t = t0 t = t0 + τ

t = t0 + 2τ t = t0 + 3τ

Semantic Pendulum (Cyclic)

A i A j T L k

T ~

Cyclic Pendulum Example

A 1 A 2 L 1 A 1 A 2

A 1 A 2 L 1 A 1 A 2

t = t0 t = t0 + 2τ

t = t0 + 5τ t = t0 + 7τ

A1 A22L1

3

Semantic Rules in SWRL

Metasemantic Algebra of Contexts

Ai

A j

Ai

A j

kL

~Lk

Semantic Operations: Inversion

),~

,(),,( ikjjki ALAPALAP

kk LL ~~

),,(),,( jkijki ALAPALAP

Semantic Operations: Negation

P(<Mary>, <to_love>, <Tom>) = false,

it is the same as:

P(<Mary>, <not_to_love>, <Tom>) = true.

kk LL kk LL~~

As

Aj

Lk

Ai

Ln As

Aj

Lk

Ai

Ln

L Lk n*

Semantic Operations: Composition

),*,(),,(),,( jnkijnsski ALLAPALAPALAP

If it is true: P(<Mary>, <has_husband>, <Tom>) and

P(<Tom>, <has_mother>, <Diana>),

then it is also true that:

P(<Mary>, <has_mother-in-law>, <Diana>).

Composition in Semantic Web

If it is true: P(<Mary>, <has_husband>, <Tom>) and

P(<Tom>, <has_mother>, <Diana>),

then it is also true that:

P(<Mary>, <has_mother-in-law>, <Diana>).

has_husband has_mother

has_mother-in-law

Aj Lk

Ai

Ln

AjAiL Lk n

Semantic Operations: Integration

),,(),,(),,( jnkijnijki ALLAPALAPALAP

<to_give_birth_to> + <to_take_care_of> = <to_be_mother_of>.

Al'

AjLkAi

Ln'

AjAiLkLn'a)

Semantic Operations: Interpretation

Al'

LkAi

Ln'

Ai

LkLn'

b)

),,(

)),,(,(),,(),,('

''''

jLki

jkillnljki

ALAP

ALAPAistALAPALAP

n

Interpretation in Semantic Web(RDF Reification)

has_polytical_opponent

has_coalition_partner

V. Yuschenko V. Yanukovich

Source: “Ukrayinska Pravda”

Source: “Obozrevatel”

agrees

agrees

level thn of metacont.about knowl....contextabout knowl.knowledge

knowledge dinterprete

Interpreting Knowledge in a Context (multiple reification)

…

Interpretation (Decontextualization)

Suppose that your colleague, whose context you know well, has described you a situation. You use knowledge about context of this person to interpret the “real” situation. Example is more complicated if several persons describe you the same situation. In this case, the context of the situation is the semantic sum over all personal contexts.

)knowledge dinterprete(knowledge received contextabout knowledgexL

Content Forecasting

Suppose that you observe some situation and know exactly what happened. Than you can guess by which way this situation will be described to you by other persons whose context you know well.

knowledge truecontextabout knowledgexL

Context Recognition

knowledge dinterpreteknowledge xL

Suppose that someone sends you a message describing the situation that you know well. You compare your own knowledge with the knowledge you received. Usually you can derive your opinion about the sender of this letter. Knowledge about the source of the message, you derived, can be considered as certain context in which real situation has been interpreted and this can help you to recognize a source or at least his motivation to change the reality.

tioninterpreta)(

alizationdecontextu)()(nL

mn

mm

Lk

Lx

Lkxk

Lx

LL

LLLL

Lifting (Relative Decontextualization)

This means deriving knowledge interpreted in some context if it is known how this knowledge was interpreted in another context.

Six Challenges for the Semantic Web

Richard Benjamins, Jesus Contreras,

Oscar Corcho, Asuncion Gomez-Perez

April 2002

• Currently, there is little Semantic Web content available. There is a need need to create a set of annotation services (middleware) concerning static and dynamic web documents, which may include multimedia, and web services.

Challenge 1: Availability of ContentChallenge 1: Availability of Content

• Constructing of kernel ontologies to be used by all the domains.

• Managing evolution of ontologies and their relation to already annotated data.

Challenge 2: Ontology Availability, Challenge 2: Ontology Availability, Development and EvolutionDevelopment and Evolution

• Once we have the Semantic Web content, we need to worry about how to manage it in a scalable manner, that is, how to organize it, where to store it and how to find the right content.

Challenge 3: Scalability of Semantic Challenge 3: Scalability of Semantic Web ContentWeb Content

• Multilinguality plays an increasing role at the level of ontologies, of annotations and of user interface.

Challenge 4: MultilingualityChallenge 4: Multilinguality

• With the increasing amount of information overload, intuitive visualization of content will become more and more important.

Challenge 5: VisualizationChallenge 5: Visualization

• WWW consortium is producing recommendations on the languages and technology that will be used in Semantic Web area.

• In order to advance the state of the art in the Semantic Web, it is important that such standards appear fast and will be adopted by the community.

Challenge 6: Semantic Web Challenge 6: Semantic Web Language StandardizationLanguage Standardization

Summary

• The semantic web is based on machine-processable semantics of data.

• Its backbone technology are Ontologies.

• It is based on new web languages such as XML, RDF, and OWL, and tools that make use of these languages.

ConclusionConclusion

• Semantic Web is not only a technologytechnology as many used to name it;

• Semantic Web is not only an environmentenvironment as many naming it now;

• Semantic WebSemantic Web it is a new contextcontext within which one should rethink and re-interpret his existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance…

“Ask not what the Semantic Web Can do for you, ask what you can do

for the Semantic Web”

Hans-Georg Stork, European Union

http://lsdis.cs.uga.edu/SemNSF