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
Finland, JyväskyläFinland, Jyväskylä
InformationInformation:: … …
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
Finland, JyväskyläFinland, Jyväskylä
InformationInformation:: … …
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
Search agent,provides “semantic match”
functionality
Shared ontology
Semantic annotation
users
Semantic CommunicationSemantic Communication (human-to-machine)(human-to-machine)
request for semantic callrequest for
semantic call
Search agent,provides “semantic match”
functionality
Search agent,provides “semantic match”
functionality
Shared ontology
Semantic annotation
Field devices
Condition Monitoring
Expert
Condition Monitoring
Expert
Semantic CommunicationSemantic Communication (machine-to-human)(machine-to-human)
request for semantic callrequest for
semantic call
Search agent,provides “semantic match”
functionality
Search agent,provides “semantic match”
functionality
Shared ontology
Semantic annotation
Fault diagnostics experts
Smart deviceSmart device
Semantic CommunicationSemantic Communication (machine-to-machine)(machine-to-machine)
request for semantic callrequest for
semantic call
Search agent,provides “semantic match”
functionality
Search agent,provides “semantic match”
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.
Search agent,provides “semantic match”
functionality
Search agent,provides “semantic match”
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)
Message from Michele
Message from Michele
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
Condition Monitoring
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
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 RealityExecutable Reality: : Executable semantic mExecutable semantic meesh-upsh-up
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