semantic web: applications

Semantic Web: ApplicationsSemantic Web: Applications

Ch Aatif Hussain Warraich

ContentContent

1. Semantic Annotation

2. Semantic Communication

3. Semantic Search

4. Semantic Integration

5. Semantic Personalization

6. Semantic Proactivity

7. Semantic Visualization

8. Semantic Games

Technology Roadmap for ApplicationsTechnology Roadmap for Applications

Semantic Web (SW)

P2P Web ServicesAgent Technology

Semantic Integration

Semantic Search

Semantic Proactivity

Semantic Games

Semantic Personalization

Machine Learning

Semantic Communication

Semantic Annotation

1

2

3

4

5

6

7

1. Semantic annotation1. Semantic annotation

Ontology-based User Interface Ontology-based User Interface

Simple user data ontology for mobile phonesSimple user data ontology for mobile phones

Model of user’s data and other resources:- Contacts (phone numbers, names etc.)- Notes (some pieces of text)- Calendar (with some events assigned)

Data to store in every instance of defined information model

Auto-generated form for data

Using generated interfaceUsing generated interface

Data view is described as an ontology which contains all needed information about data structure. User interface is built dynamically from ontology:• Fields for data• Form layout, types of controls (e.g. picture, checkboxes etc.)• Rules for data that can check some constraints, invoke actions, perform calculations – whatever!

For described data model forms are generated

Access your data quickly and easilyAccess your data quickly and easily……

Contact data

Event data

Possibilities to build flexible, easily customizable data management applications are great.

select to open another form

Every piece of data is somehow described in user’s terms from data-view ontology.Links between data make it easy to find any needed information

Contact data

List ofcontacts

Browsing the annotated dataBrowsing the annotated data

Using image metadata for browsing Using image metadata for browsing and linking to other dataand linking to other data

WorkshopWorkshop – IOG & MetsoIOG & Metso

12/04/200312/04/2003

Finland, JyväskyläFinland, Jyväskylä

InformationInformation:: … …

Vagan TerziyanVagan Terziyan 12/04/20312/04/203



Part ofPart of <image: Workshop – IOG & Metso><image: Workshop – IOG & Metso>LinkLink toto <Vagan Terziyan><Vagan Terziyan>

WorkshopWorkshop – IOG & MetsoIOG & Metso 12/04/200312/04/2003Finland, JyväskyläFinland, JyväskyläInformationInformation:: … …LinkedLinked toto:: < <imageimage: Vagan Terziyan>: Vagan Terziyan>

<<imageimage: Jouni Pyötsiä>: Jouni Pyötsiä><<imageimage: Oleksiy Khriyenko>: Oleksiy Khriyenko><<imageimage: Andriy Zharko>: Andriy Zharko><<imageimage: Oleksandr Kononenko>: Oleksandr Kononenko>

NameName: : Vagan TerziyanVagan Terziyan Sex: Sex: MaleMale

Date of BirthDate of Birth: : 27 December,27 December, 19581958CitizenshipCitizenship: : UkraineUkraine

Phone: Phone: +358 14 260 3011+358 14 260 3011E-mail: E-mail: [email protected] [email protected]

URL: URL: www.cs.jyu.fi/ai/vaganwww.cs.jyu.fi/ai/vagan

……

Oleksiy Khriyenko 12/04/20312/04/203



Part ofPart of <image: Workshop – IOG & Metso><image: Workshop – IOG & Metso>LinkLink toto <Oleksiy Khriyenko><Oleksiy Khriyenko>

Select images by:Select images by:- Date- Date

- Link:- Link:- Place (location)- Place (location)

- …- …

……

……<Oleksiy Khriyenko><Oleksiy Khriyenko>

Location based image annotationLocation based image annotation

Storing of the Historical Dynamics

of the places (areas)

Hotspots

Location based Information Service

GPS system

Request for location

Location

area/coordinate

Request for location based information (via coordinate/area)

Information about area (description)

Spain, the memorial off

”XXX” London, Thames bank.

Near the ”Big” bridge.

Date: 27/03/2004

Additional Information:

<for personal infill>

FinlandJyväsky

lä Agora

Location based Photo Album-Map Location based Photo Album-Map

AgoraAgora

FinlandFinlandJyväskyläJyväskylä

13/08/200313/08/2003

Information: …Information: …Make a image trip map:Make a image trip map:

- day- day

- month- month

- year- year

……

……NokiaNokia

FinlandFinlandJyväskyläJyväskylä

13/08/200313/08/2003

Composing Photo Albums Composing Photo Albums using metadatausing metadata

USER 1

USER 2

USER N

“My Friends” “Wedding” “Workgroup” “Our Holidays”

Web server

BANKBANK: Data : Data annotationannotation

In order to make miscellaneous data gathered and used later for some processing,every piece of data needs label assigned, which will denote its semantics in terms ofsome ontology. Software that is developed with support of that ontology can recognize the data and process it correctly in respect to its semantics.

Ontology of gathered data

Web forms and dialogs generated

An

no

tate

d d

ata

(RD

F)

Processing of data by some other semantic-aware applications

2. Semantic Communication2. Semantic Communication

Semantic CallSemantic Call

Call to a person, who can satisfy my needs/requirements.

Needs: Buy

Car

what

BMW 318imodel

1995 - …

age

<= 250000

mileage

<= 7500 e

price

Finland

my location

SEMA – semantic profile based matching service

Call to a person, who can satisfy my needs/requirements.

Needs: Sell

Car

what

BMW 318imodel

1998age

150000mileage

7000 e

price

Finland

my location

Needs: Sell

Car

whatBMW 318i

model1998age

150000mileage

7000 eprice

Finland

my location

Needs: Buy

Carwhat

BMW 318imodel

1995 - …age

<= 250000

mileage

<= 7500 eprice

Finland

my location

Semantic Match of the Profiles

High Level of Privacy.

IDs

Phone Numbers AddressesNO

Interests

Profile BusinessJUST

Semantic CommunicationSemantic Communication (human-to-human)(human-to-human)

useruserrequest for

semantic callrequest for

semantic call

Search agent,provides “semantic match”

functionality


functionality

Shared ontology

Semantic annotation

users

Semantic CommunicationSemantic Communication (human-to-machine)(human-to-machine)

request for semantic callrequest for

semantic call


functionality


functionality

Shared ontology

Semantic annotation

Field devices

Condition Monitoring

Expert


Expert

Semantic CommunicationSemantic Communication (machine-to-human)(machine-to-human)


semantic call


functionality


functionality

Shared ontology

Semantic annotation

Fault diagnostics experts

Smart deviceSmart device

Semantic CommunicationSemantic Communication (machine-to-machine)(machine-to-machine)


semantic call


functionality


functionality

Shared ontology

Semantic annotation

Field devices

Smart deviceSmart device

Semantic CallSemantic Call

• Examples:“Connect me with someone who can sell

me cheep (< 500) rowing boat in Jyväskylä”

“Connect me with a blond girl (21-25) who wants to meet a guy (26) tonight to go to dancing club in Jyväskylä”, etc.

Public merchants, public customers, public information providers

…

…

Clients

SMOs

SMRs

Maps <path network>

Maps <business points>

Integration, Analysis, Learning

Business Ontology

Server

I

C I

I S I

Negotiation, Contracting,

Billing

Meta-Profiles

Profiles

RDF

Location Providers

Server

Map Content Providers

Server

Content Providers

Server

…

…

…

External Environment

RDF

$ $ $ Banks

Architecture for a Mobile P-Commerce ServiceArchitecture for a Mobile P-Commerce Service

Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.

3. Semantic Search3. Semantic Search

Semantic Web: Semantic SearchSemantic Web: Semantic Search

useruserrequest for

semantic searchrequest for

semantic search

Shared ontology

Semantic annotation

Web resources / services / DBs / etc.


functionality


functionality

Semantic SearchSemantic Search

What to search?

data (images, image fragments, video,

etc.)

persons

places

services

…whatever that can be annotated and accessed.

Where to search?

Standalone phone;

Local server;

Peer-to-Peer;

shared environment;

global Web.

Semantic Facilitators for Web Semantic Facilitators for Web Information Retrieval (2004)Information Retrieval (2004)

1. Generic Semantic Search Facilitator concept, architecture and ideas for future utilization of semantic wrappers for non-semantic search systems

2. Implementation of Semantic Search Assistant for Google with semantic interface and domain ontology.

InBCT Tekes PROJECT Chapter 3.1.3 :InBCT Tekes PROJECT Chapter 3.1.3 :“Industrial Ontologies and Semantic Web” (year 2004)“Industrial Ontologies and Semantic Web” (year 2004)

How does it work?How does it work?

1. Get request

2. Translate request into series of queries to the used search engines, databases, data storages… Taking into account the semantics of searched data

3. Combine returned results, filter non-relevant (if keyword search was used) results

4. Return set of best-try results

Sense DeterminationSense Determination

• WordNetWordNet is an open source ontology, which contains information about different meanings of a term, synonyms, antonyms and other lexical and semantic relations

• Having several words in search query we can determine in which context (sense) each of them is used with the help of WordNet: by comparing words synsets by comparing words textual descriptions and

examples by finding common roots going up in WordNet

hierarchy tree for each word by asking a user

Semantic Search AssistantSemantic Search Assistant

How does it work?How does it work?

1. Gets keyword query 2. Translates original query into series of

queries to Google taking into account the semantics of keywords

3. Combines returned results

Ontology Ontology Personalization:Personalization:

is mechanism, which is mechanism, which allows users to have allows users to have own conceptual view own conceptual view and be able to use it for and be able to use it for semantic querying of semantic querying of search facilities. search facilities.

“Driver”

“Driver”

“Driver”

“Driver”“Driver”

Common ontologyCommon ontology

SSearchearch

Ontology PersonalizationOntology Personalization

WordNet 2.0 Search ExampleWordNet 2.0 Search Example• Search word: "driver“ The noun "driver" has 5 senses in WordNet.

1. driver -- (the operator of a motor vehicle)2. driver -- (someone who drives animals that pull a vehicle)3. driver -- (a golfer who hits the golf ball with a driver)4. driver, device driver -- ((computer science) a program that determines how a computer will communicate with a peripheral device)5. driver, number one wood -- (a golf club (a wood) with a near vertical face that is used for hitting long shots from the tee)

• Sense 1driver -- (the operator of a motor vehicle) => busman, bus driver -- (someone who drives a bus) => chauffeur -- (a man paid to drive a privately owned car) => designated driver --(the member of a party who is designated to refrain from alcohol and so is sober when it is time to drive home) => honker -- (a driver who causes his car's horn to make a loud honking sound; "the honker was fined for disturbing the peace") => motorist, automobilist -- (someone who drives (or travels in) an automobile) => owner-driver -- (a motorist who owns the car that he/she drives) => racer, race driver, automobile driver -- (someone who drives racing cars at high speeds) …

Generating of requests setGenerating of requests set

• WordNet API and dictionaries are used for generating the set of requests

• When user enters original request, SSA switches to the panel, where different senses of typed word are presented

Semantic Search Enhancement :Semantic Search Enhancement :Common (linguistic) Common (linguistic)

ontologyontology

QueryQuery : : ( ( XX XX XX XX XXXX XXXX XX XX ))

Domain ontologyDomain ontology

SemanticFilteringSemanticFiltering

Result:Result:

Enabling the Semantic SearchEnabling the Semantic Search

Semantic Search Assistant Semantic Search Assistant (Facilitator) uses ontologically (Facilitator) uses ontologically (WordNet) defined knowledge about (WordNet) defined knowledge about words and embedded support of words and embedded support of advanced Google-search query features advanced Google-search query features in order to construct more efficient in order to construct more efficient queries from formal textual description queries from formal textual description of searched information. Semantic of searched information. Semantic Search Assistant hides from users the Search Assistant hides from users the complexity of query language of complexity of query language of concrete search engine and performs concrete search engine and performs routine actions that most of users do in routine actions that most of users do in order to achieve better performance order to achieve better performance and get more relevant results.and get more relevant results.

ExampleExample

• Initial query: hotel reservation agency

(1, 7 and 5 senses correspondingly)

• From first 5 results only 3 are relevant(results with whole sequence of query words even does not appear in first three pages)

• Generated query:("hotel") ("booking" OR

"reserve") (-"qualification") ("bureau" OR "agency") (-"means")

• From first 5 results all are relevant

(using synonym “booking” along with “reservation” was helpful)

Semantic Search of PeopleSemantic Search of People

Searching persons in a P2P environment

Preferences: blond single girl, weight:45-65 kg, height 160-180 sm.

Blond single girl, weight:50 kg, height 170

sm.match

People gathered for a meeting can browse shared data of each other

• Every data object/fragment has associated semantic annotation, which makes possible data filtering

• Data sharing in big crowds can be performed in the ad-hoc manner (chain messages).

4. Semantic Integration4. Semantic Integration

Semantic Web: Semantic Web: Semantic IntegrationSemantic Integration

Integrated resource

Shared ontology

Semantic annotation

Web resources / services / DBs / etc.

Invite you and <text: name

of recipient’s wife> to celebrate house-

warming <image: my house> today at 20:00. Our address is <text: my

address> <image: map of my

address>. With the best regards, Crawford family

<image: my family> <voice: welcome>

Message for John

Message for XXX Invite you and < text : your wife> to celebrate house-

warming < image : my house> today at 20:00. Our

addres is < text : my addres> < image : map of my addres>. With the best

regards, family YYY < image : our family> < voice

: welcome>

UR

IsU

RIs

Integrated documentIntegrated document: : SmartMessageSmartMessage

Resource description via personal ontology

<tag>

?…

“Mood” of the message

recipient – “John” wife

sender address

sender house

Roninmäentie 5T 23

sender address map

sender family

Semantic Search

Invite you and <text: name

of recipient’s wife> to celebrate house-

warming <##URI> today at 20:00. Our address is

<##URI> <##URI> . With the best regards, Crawford

family <##URI> <##URI>

Message from Michele

<##URI>

Ob

jec

tsO

bje

cts

Sender - Michele

??

Sender - Michele

Sender - Michele

Sender - Michele

recipient wife name – “July”

address

house

address map

family

??

??

??

??

Semantic Search

SMS

Request for objects

MMS (objects)



Invite you and July to celebrate house-warming

With the best regards, Crawford

family

today at 20:00. Our address is

Roninmäentie 5T 23

Invite you and July to celebrate house-warming

With the best regards, Crawford family

today at 20:00. Our address is Roninmäentie 5T 23

Corporate/Business HubCorporate/Business Hub

Publish own resource descriptions

Advertise own services

Lookup for resources with semantic searchAutomated access to enterprise (or partners’) resources

Hub ontologyand shared domain ontologies

Seamless integration of services

Software and data reuse

Partners / Businesses

What parties can do:What parties achieve:

Ontologies will help to glue such Enterprise-wide / Cooperative Semantic Web of shared resources

Companies would be able to create “Corporate Hubs”, which would be an excellent cooperative business environment for their applications.

OntonutsOntonuts as a tool for semantic integration as a tool for semantic integration

Ontonuts: Competence Profile of an Agent as a Ontonuts: Competence Profile of an Agent as a service provider (“what can I do” and “what can I service provider (“what can I do” and “what can I

answer”) and appropriate service plan (“how I do … answer”) and appropriate service plan (“how I do … or answer …”)or answer …”)

You can

ask me for …

a) … actionb) …

informationontonut

External view to ontonuts: Shared External view to ontonuts: Shared Competence SpecificationCompetence Specification

You can

ask me for …

a) I know everything about Mary

b) I know everything about cats

c) I know what t ime it is now

d) I know all lovers of Johne) I know grades on

chemistry of all pupils from 4-B

a) I can open the door #456

b) I can flyc) I can use knifesd) I can build house from

woode) I can visualize mapsf) I can grant access to

folder “444”We consider ONTONUTS to be shared S-APL specifications of these

competences

External

Internal

Internal view to ontonuts: Action or Query Internal view to ontonuts: Action or Query PlansPlans

You can

ask me for …

a) I know everything about Mary

S-APL plan of querying either own beliefs or external database about Mary

a) I can open the door #456

S-APL plan of opening the door #456

We consider ONTONUTS to be also an internal plans to execute competences

External

Internal

Possible general rule of ontonut appearancePossible general rule of ontonut appearance

You can

ask me for …

IF I have the plan how to perform certain complex or simple action or the plan how to answer complex or simple query

AND {t ime-to-time execution of the plan is part of my duty according to my role (commitment) OR I am often asked by others to execute action or query according to this plan}

THEN I wil l create ONTONUT which wil l make my competence on this plan explicit and visible to others

External

Internal

Example (1): Atomic Ontonut #1Example (1): Atomic Ontonut #1

City X Central Hospital

Relational Database

I can answer any queries on

mental diseases of citizens of X

I know how appropriate database is

organized, I have access rights and I am able to query it

Give me the list of women from X with mental diseases diagnosed after

2006

Example (2): Atomic Ontonut #2Example (2): Atomic Ontonut #2

Nordea XML

Database

I can answer any queries on loans in Nordea

bank

I know how appropriate database is

organized, I have access rights and I am able to query it

Give me the list of Nordea clients with loans of more than 100 000 EURO

Example (3): Complex Ontonut #3Example (3): Complex Ontonut #3

I can answer any queries on mental diseases and loans of Nordea bank clients from X

I know how to split query to two components; I know to whom I can

send component queries (I have contracts with them); and I know

how to integrate outcomes of these queries

Give me the list of Nordea clients from X with loans of more than 200 000 EURO and who

has more than 2 mental disorders during last 5 years

Industrial Resource Lifecycle and HistoryIndustrial Resource Lifecycle and History

States Symptoms

DiagnosesMaintenance Plan

Measurement

Data Warehousing

Predictive Measureme

nt


Diagnostics

Maintenance Planning

Predictive Monitorin

g

Conditions Warehousin

g

Predictive Maintenanc

e Plan Warehousin

g

Predictive Diagnostics

Diagnoses Warehousin

g

Industrial Resource

HistoryHistory

Fault detection,

alarms

Faultidentification,localizationFault

isolation

RDFRDF

RDFRDF

Maintenance

5. Semantic Personalization5. Semantic Personalization

18

Multimeetmobile Project (2000-2001)

Information TechnologyResearch Institute(University of Jyvaskyla):Customer-oriented research anddevelopment in Information Technology

http://www.titu.jyu.fi/eindex.html

Multimeetmobile (MMM) Project(2000-2001):Location-Based Service System and TransactionManagement in Mobile Electronic Commerce

http://www.cs.jyu.fi/~mmm

Academy of FinlandProject (1999):Dynamic Integration ofClassification Algorithms

Mobile Location-Based Service in Mobile Location-Based Service in Semantic WebSemantic Web

19

M-Commerce LBS systemhttp://www.cs.jyu.fi/~mmm

In the framework of the Multi Meet Mobile(MMM) project at the University of Jyväskylä,a LBS pilot system, MMM Location-basedService system (MLS), has been developed.MLS is a general LBS system for mobileusers, offering map and navigation acrossmultiple geographically distributed servicesaccompanied with access to location-basedinformation through the map on terminal’sscreen. MLS is based on Java, XML and usesdynamic selection of services for customersbased on their profile and location.

Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,Developing GIS-Supported Location-BasedServices, In: Proceedings of WGIS 2001 - FirstInternational Workshop on Web GeographicalInformation Systems, 3-6 December, 2001, Kyoto,Japan, pp. 423-432.

20

Adaptive interface for MLS client

Only predicted services, for the customer with known profileand location, will be delivered from MLS and displayed atthe mobile terminal screen as clickable “points of interest”

21

Route-based personalization

Static Perspective Dynamic Perspective 22

Inductive learning of customerpreferences with integration of predictors

>→< rrmrr yxxx ,...,, 21

Sample Instances

>< tmtt xxx ,...,, 21

yt

Learning Environment

P1 P2 ... Pn

Predictors/Classifiers

Terziyan V., Dynamic Integration of Virtual Predictors, In: L.I. Kuncheva, F.Steimann, C. Haefke, M. Aladjem, V. Novak (Eds), Proceedings of the International ICSCCongress on Computational Intelligence: Methods and Applications - CIMA'2001, Bangor,Wales, UK, June 19 - 22, 2001, ICSC Academic Press, Canada/The Netherlands, pp. 463-469.

Contextual and Predictive AttributesContextual and Predictive Attributes

>< −

featureslocation

imim

featuresprofile

ii xxxx

1

21 ,...,... ,,

iy

Mobile customerdescription

Ordered service

Contextualattributes

Predictiveattributes

Simple distance between Two Preferences with Simple distance between Two Preferences with Heterogeneous Attributes Heterogeneous Attributes (Example)(Example)

∑∈∈∀

⋅=YyXxi

iii

ii

yxdYXD,,

2),(),( ω

− =

−=

i

ii

ii

ii

range

yx

yxi

yxd

:else

otherwise ,1

if ,0 - nominal is attributeth if

),(

where:

d (“white”, “red”) = 1

d (15°, 25°) = 10°/((+30°)-(+10°)) = 0.5

Wine Preference 1:

I prefer white wine served at 15° C

Wine Preference 2:

I prefer red wine served at 25° C

Importance:Wine color: ω1 = 0.7

Wine temperature: ω2 = 0.3

D (Wine_preference_1, Wine_preference_2) = √ (0.7• 1 + 0.3 • 0.5) ≈ 0.922

64

Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 1

∑∈∈∀

⋅=YyXxi

iii

ii

yxdYXD,,

2),(),( ω

where:

P(wine|colour = white) =

= 100 / 500 = 0.2

P(wine|colour = red) =

= 200 / 300 = 0.67

−−

−−=

∑=

numerical. is attributeth if,||

nominal; is attributeth if,)]|()|([

),( 1

2

irange

yx

iycPxcP

yxd

i

ii

C

cii

ii

Domain objects: 1000 drinks;300 red, 500 white, 200 - other

Soft drinks: 600;100 red, 400 white, 100 - other

Wines: 400;200 red, 100 white, 100 - other

P(soft_drink|colour = white) =

= 400 / 500 = 0.8

P(soft drink|colour = red) =

= 100 / 300 = 0.33

65

Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 2

∑∈∈∀

⋅=YyXxi

iii

ii

yxdYXD,,

2),(),( ω

where:

P(wine|colour = white) = = 100 / 500 = 0.2P(wine|colour = red) = = 200 / 300 = 0.67

−−

−−=

∑=

numerical. is attributeth if,||

nominal; is attributeth if,)]|()|([

),( 1

2

irange

yx

iycPxcP

yxd

i

ii

C

cii

ii

P(soft_drink|colour = white) = = 400 / 500 = 0.8P(soft drink|colour = red) = = 100 / 300 = 0.33

d (“white”, “red”) = √ [(P(soft_drink|colour = white) - P(soft drink|colour =

red) )2 + + (P(wine|colour = white) - P(wine|colour = red) )2 ] = = √ [(0.8 – 0.33 )2 + (0.2 – 0.67 )2 ] ≈ 0.665

D (Wine_preference_1, Wine_preference_2) = √ (0.7• 0.665 + 0.3 • 0.5) ≈ 0.784

Prediction of Customer’s ActionsPrediction of Customer’s Actions

d1 d2

d3

d4

d5

here I washed my car

here I had nice wine here I had massage

here I had great pizza

here I made hair

I am here now.There are my recent preferences:1. I need to wash my car: 0.12. I want to drink some wine: 0.23. I need a massage: 0.24. I want to eat pizza: 0.85. I need to make my hair: 0.6 Make a guess what I will order now and where !

Smart assistantSmart assistant

Preferences

(semantic profile)

Assumtion:

”users like what they photograph”

Somewhere in the other places

Nearby there is a wounderful old

castle!

Advices based on automatically collected preferences

(photographing)

Conclusion:

”users likes old castles”

location-based annotations

location awareness

Smart assistantSmart assistantAdvices based on configured

preferences

food ordered through mobile phone

I like it

…saving to the history…

clothes with scannable ID

any other objects with accessible semantic profile

location-based annotations

searching matches

Nearby supermarket (Kauppakatu 7) has shirts that you like

so much

6. Semantic Proactivity6. Semantic Proactivity

Intelligent answering machineIntelligent answering machine

Sometimes users cannot answer the

income callAway for sports

Visiting important meeting

Making presentation or lecturing

Studying

Sleeping

A phone was lost or stolen

Intelligent answering machineIntelligent answering machine

Sometimes users cannot answer the

income callAway for sports

Visiting important meeting

Making presentation or lecturing

Studying

Sleeping

A phone was lost or stolen …however they can

configure intelligent answering machine

schedule data location data

The user is currently at the swimming pool, Ontokatu 12. He/she will be there until 12 a. m.

If boss or parents call, wake up.

wake up! I need you today!

location datacamera phone

schedule data

location datacamera phone

Detalization of the reply can be configured depending on the calling person:

Sorry, buddy, I’m busy now. I’m at the university, we have a meeting with colleagues.

friend

We have a meeting at auditorium 2. It started at 14-00 and will last until 16.35 p. m. Here is its photo.

colleague

wife

I’m at the university, sunny. We (Vagan, Sasha, Ljosha) have a meeting at the auditorium 2. Here is a detailed map and photo of the event.

GUN Concept:GUN Concept: All GUN resources “understand” each otherAll GUN resources “understand” each other

Real World

objects

OntoAdapters

Real World Object ++ OntoAdapter +

+ OntoShell == GUN ResourceGUN Resource

GUNGUN

OntoShells

Real World objects of new generation (OntoAdapter inside)

Web Services for Smart DevicesWeb Services for Smart Devices

Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc.

OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, March 2003,

Global Network of Maintenance ServicesGlobal Network of Maintenance Services

OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, March 2003,

On-line learning

On-line learning

Smart Maintenance EnvironmentSmart Maintenance Environment

““Devices with Devices with on-line data”on-line data”

““Experts”Experts”

Maintenance

Maintenanceexchangexchang

eedatadata

Maintenance

Maintenance

datadata

exchange

exchange

““Services”Services”

““Human/patient with embedded medical sensors ””

““DoctorDoctor//ExpertExpert””

““Medical Web Medical Web Services”Services”““Web Services Web Services for environmental for environmental

diagnostics and predictiondiagnostics and prediction””

““ExpertsExperts in environmental in environmental

monitoringmonitoring””

““Environment

with sensors ””

““Staff/studentsStaff/students

with monitored organizational data””

““Web Services Web Services in in organizational diagnostics and organizational diagnostics and

managementmanagement””

““ManagerManager//ExpertExpert””

What is UBIWARE (in short)What is UBIWARE (in short)

• UBIWARE is a tool to support:UBIWARE is a tool to support: design and installation of…,design and installation of…, autonomic operation of… and autonomic operation of… and interoperability among…interoperability among…

• … … complex, heterogeneous, open, dynamic complex, heterogeneous, open, dynamic and self-configurable distributed industrial and self-configurable distributed industrial systems;…systems;…

• … … and to provide following services for and to provide following services for systemsystem components:components: adaptation;adaptation; automation; automation; centralized or P2P organization;centralized or P2P organization; coordination, collaboration, interoperability and negotiation;coordination, collaboration, interoperability and negotiation; self-awareness, communication and observation;self-awareness, communication and observation; data and process integration;data and process integration; (semantic) discovery, sharing and reuse.(semantic) discovery, sharing and reuse.

Current UBIWARE Agent ArchitectureCurrent UBIWARE Agent Architecture

S-APLS-APL – Semantic Agent Programming Language (RDF-based)

http://users.jyu.fi/~akataso/sapl.html

Key Components of UBIWARE Key Components of UBIWARE Scientific ImpactScientific Impact

3. Language3. Language

1. UBIWARE: 1. UBIWARE: Approach and Approach and ArchitectureArchitecture

2. Engine2. Engine

4. Ontonuts4. Ontonuts

Business Process Business Process ChoreographyChoreography

External External Capabilities Capabilities

OrchestrationOrchestration

Semantic Integration in UBIWARE 3.0

Presentation Case for UBIWARE 3.0

X1 :firstName :VaganX1 :lastName :TerziyanX1 :sex :MaleX1 :birthday :27/12/1958X1 :email :[email protected] :interest :fishingX1 :hasPhoto #vagan.jpgX1 :group :IOGX1 :group :RuleML…X1 :education :KNUREX1 :position :professorX1 :hasFriend X2X2 :firstName :AlainX2 :lastName :Gourdin…X1 :hasFriend X3X3 :firstName :MikkoX3 :lastName :Vapa…

Linked Data

EnvironmentEnvironment

HardBodyHardBody

SoftBodySoftBody

SoftMindSoftMind

HardMindHardMind

HardSoulHardSoul

SoftSoulSoftSoul

UBIWARE Agent: Possible Future ArchitectureUBIWARE Agent: Possible Future Architecture

RABRAB – Reusable Atomic Behavior

RBERBE – Reusable Behavior Engine

RR AA BB RR AA BB RR AA BB RR AA BB

RR BB EE RR BB EE RR BB EE RR BB EE

BeliefsBeliefs(facts, rules, policies, plans)(facts, rules, policies, plans)

Shared Shared BeliefsBeliefs

Shared Shared RABsRABs

Shared Shared RBEsRBEs

Shared Shared Meta-BeliefsMeta-Beliefs

Meta-Beliefs Meta-Beliefs (preferences)(preferences)

““Life” BehaviorLife” Behavior

Co

nfig

ura

tion

Co

nfig

ura

tion (G

EN

OM

E)

(GE

NO

ME

)

Shared Shared HardwareHardware

“Visible” to other agents

through observation

Ontobil i tyOntobil i ty is self-contained, self-described, semantically marked-up proactive agent capability (agent-driven ontonut), which can be “seen”, discovered, exchanged, composed and “executed” (internally or remotely) across the agent platform in a task-driven way and which can perform social utility-based behaviorGenomeGenome is part of semantically marked-up agent configuration settings, which can serve as a tool for agent evolution: inheritance crossover and mutation

May be an

agent

7. Semantic Visualization7. Semantic Visualization

This is not simpleThis is not simple

Table (ID3)

hasColor (ID1, “Green”)

Ball (ID2)

hasColor (ID2, “Red”)

isOnTheLeftSideOf (ID2, ID1)

hasTemperatureC (ID1, 30)

hasTemperatureC (ID2, 25)

isOn (ID1, ID3)

isOn (ID2, ID3)

Cube (ID1)

hasColor (ID3, “Brown”)

isLarger (ID2, ID1)

30°

25°

Semantic Mash-Up engineSemantic Mash-Up engine

“In the idea of a semantic mash-up, the mash-up program is a model-driven architecture. This puts the structure of the mash-up under model control, rather than program control. It is still necessary to translate each information source into a semantic structure (i.e., RDF), but once that has been done, the structure of the mash-up is specified by a model, rather than by program code” [TopQuadrant Inc, June 2007]. http://jazoon.com/jazoon07/en/conference/presentationdetails.html?type=sid&detail=870

needs also context-based relevant

features selection

PersonPerson

Healthcare

Organs’ condition

Employer based

Location of healthcare organization

Person-location based

Work

Work-place location

Members, training facilities (stadium)

Training teams, football field, …

Football teamFootball team

StadiumStadiumPersonPerson

Human heartHuman heart

PersonPerson

Family

relation

Family relation

Condition

Internal

Consisting of external systems

Work

Occupation, profession

Treatment of cardiovascular disease

Medical center location

The visualization of a “human heart” resource in a context of its internal condition can be introduced in a form of internal structure of the heart and its functional parts.

“Human heart” resource in a context of its condition in relation to other human body systems can be visualized as a part of an internal structure of a human body.

The visualization of a “person” resource in a context of healthcare & condition of one’s organs can be performed in a way of human body diagram (with a view of the organs).

At the same time, ”person” resource in a context of healthcare & location of a healthcare organization can be visualized in a form of a map.

The visualization of a “person” resource in a context of family relations can be displayed in a form of family tree visualization.

The occupation/profession-based visualization of a “person” resource. Visualization of a working area with the relevant work-related links: duties, area of interests, professional related resources, contacts, etc.

Occupation, profession

4i (“for eye”) 4i (“for eye”) Semantically enhanced context-based multidimensionalSemantically enhanced context-based multidimensional

Resource Visualization Resource Visualization (O. Khriyenko)(O. Khriyenko)

Executable RealityExecutable Reality: : Executable semantic mExecutable semantic meesh-upsh-up


115

Terziyan V., Kaykova O., Towards "Executable Reality”: Business Intelligence on Top of Linked Data, In: Proceedings of the First International Conference on Business Intelligence and Technology (BUSTECH-2011), September 25-30, 2011, Rome, Italy, IEEE CS Press, pp. 26-33.


Executable Focus

University of Jyvaskyla

On-the-fly generated statistics

Contexts

Contexts for BI services

Terziyan V., Kaykova O., Towards "Executable Reality”: Business Intelligence on Top of Linked Data, In: Proceedings of the First International Conference on Business Intelligence and Technology (BUSTECH-2011), September 25-30, 2011, Rome, Italy, IEEE CS Press, pp. 26-33.

8. Semantics-Enabled Games8. Semantics-Enabled Games

MetaGame: MetaGame: semantically annotated episodessemantically annotated episodes

Semantic Match

Semantical Games Space

MetaGameMetaGameOn-line Semantic

Composition of the Games

Unified Game Profile of a PlayerUnified Game Profile of a Player• Saved game data (game state, user level, points, etc.) can be shared

between many heterogeneous games via common annotation of data with game ontologies

• Changing games does not mean to change to become a new player…

RDFS/RDF data storageGame Profile ontologies

Personalization of gamesPersonalization of games

Games can be designed in a way that they have standardized (semantic) descriptions of the customizable elements (images,text, settings) that can be manually/automatically changed to the preferred by user. Semantic annotation helps better finding matches between what can be customized and what should be customized

Customized images

Exercise storage

Game Assistant

Education Support GamesEducation Support Games

Home Exercise Home Exercise

Home Exercise

Home Exercise

Go to the next Go to the next levellevel

You should You should make some make some

exercisesexercises

5 + 23 = 5 + 23 = ??

131 – 94 = 131 – 94 = ??

2 * 5 = 2 * 5 = ??

Mathematics

Geography

History

Biology

semantic web: applications

Software

dataview ontology

data structure

semantic gamesfile

semantic annotation2

semantic communication3

semantic search4

semantic integration5

semantic personalization6