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SSeemmaannttiicc WWeebb:: AApppplliiccaattiioonnss 
Ch Aatif Hussain 
Warraich
CCoonntteenntt 
1. Semantic Annotation 
2. Semantic Communication 
3. Semantic Search 
4. Semantic Integration 
5. Semanti...
Technology RRooaaddmmaapp ffoorr AApppplliiccaattiioonnss 
Semantic 
Search 
2 
P2P Agent Technology Web Services 
Semanti...
11.. SSeemmaannttiicc aannnnoottaattiioonn
OOnnttoollooggyy--bbaasseedd UUsseerr IInntteerrffaaccee 
SSiimmppllee uusseerr ddaattaa oonnttoollooggyy ffoorr mmoobbiil...
UUssiinngg ggeenneerraatteedd iinntteerrffaaccee 
For described data model 
forms are generated 
Data view is described as...
Access yyoouurr ddaattaa qquuiicckkllyy aanndd eeaassiillyy…… 
Contact data 
Event data 
Possibilities to build 
flexible,...
Browsing tthhee aannnnoottaatteedd ddaattaa
Using image mmeettaaddaattaa ffoorr bbrroowwssiinngg 
aanndd lliinnkkiinngg ttoo ootthheerr ddaattaa 
WWoorrkksshhoopp 112...
Location bbaasseedd iimmaaggee aannnnoottaattiioonn 
Storing of the 
Historical Dynamics 
of the places (areas) 
Hotspots ...
Location bbaasseedd PPhhoottoo AAllbbuumm--MMaapp 
FFiinnllaanndd 
JJyyvväässkkyyllää 
Finland 
Jyväsky 
lä 
Agora 
FFiinn...
CCoommppoossiinngg PPhhoottoo AAllbbuummss 
uussiinngg mmeettaaddaattaa 
USER 1 
USER 2 
USER N 
Web server 
“My Friends” ...
BBAANNKK:: DDaattaa aannnnoottaattiioonn 
In order to make miscellaneous data gathered and used later for some processing,...
22.. SSeemmaannttiicc CCoommmmuunniiccaattiioonn
SSeemmaannttiicc CCaallll 
Call to a person, who can satisfy 
my needs/requirements. 
Needs: Buy 
Car 
what 
model BMW 318...
SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((hhuummaann--ttoo--hhuummaann)) 
uusseerr 
request for 
semantic call 
Search...
SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((hhuummaann--ttoo--mmaacchhiinnee)) 
request for 
semantic call 
Search agent...
SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((mmaacchhiinnee--ttoo--hhuummaann)) 
request for 
semantic call 
Search agent...
SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((mmaacchhiinnee--ttoo--mmaacchhiinnee)) 
request for 
semantic call 
Search a...
SSeemmaannttiicc CCaallll 
• Examples: 
“Connect me with someone who can sell 
me cheep (< 500) rowing boat in 
Jyväskylä...
Clients 
Public merchants, 
AArrcchhiitteeccttuurree ffoorr aa MMoobbiillee PP--CCoommmmeerrccee SSeerrvviiccee 
public cu...
33.. SSeemmaannttiicc SSeeaarrcchh
SSeemmaannttiicc WWeebb:: SSeemmaannttiicc SSeeaarrcchh 
uusseerr 
request for 
semantic search 
Shared 
ontology 
Semanti...
SSeemmaannttiicc SSeeaarrcchh 
What to search? 
data (images, image 
fragments, video, 
etc.) 
persons 
places 
services 
...
SSeemmaannttiicc FFaacciilliittaattoorrss ffoorr WWeebb 
IInnffoorrmmaattiioonn RReettrriieevvaall ((22000044)) 
IInnBBCCT...
HHooww ddooeess iitt wwoorrkk?? 
1. Get request 
2. Translate request into series of queries 
to the used search engines, ...
SSeennssee DDeetteerrmmiinnaattiioonn 
• WWoorrddNNeett is an open source ontology, which 
contains information about diff...
SSeemmaannttiicc SSeeaarrcchh AAssssiissttaanntt
HHooww ddooeess iitt wwoorrkk?? 
1. Gets keyword query 
2. Translates original query into series of 
queries to Google tak...
OOnnttoollooggyy 
PPeerrssoonnaalliizzaattiioonn:: 
iiss mmeecchhaanniissmm,, wwhhiicchh 
aalllloowwss uusseerrss ttoo hha...
WWoorrddNNeett 22..00 SSeeaarrcchh EExxaammppllee 
• Search word: "driver“  The noun "driver" has 5 senses in WordNet. 
1...
GGeenneerraattiinngg ooff rreeqquueessttss sseett 
• WordNet API 
and dictionaries 
are used for 
generating the 
set of r...
SSeemmaannttiicc SSeeaarrcchh EEnnhhaanncceemmeenntt :: 
CCoommmmoonn ((lliinngguuiissttiicc)) 
oonnttoollooggyy 
DDoommaa...
EExxaammppllee 
• Initial query: 
hotel reservation agency 
(1, 7 and 5 senses correspondingly) 
• From first 5 results on...
SSeemmaannttiicc SSeeaarrcchh ooff PPeeooppllee 
Searching persons in a P2P 
environment 
Preferences: blond single 
girl,...
44.. SSeemmaannttiicc IInntteeggrraattiioonn
SSeemmaannttiicc WWeebb:: SSeemmaannttiicc IInntteeggrraattiioonn 
Integrated resource 
Shared 
ontology 
Semantic 
annota...
IInntteeggrraatteedd ddooccuummeenntt:: SSmmaarrttMMeessssaaggee 
Message for John 
Invite you and <text: name 
of recipie...
CCoorrppoorraattee//BBuussiinneessss HHuubb 
Publish own resource descriptions 
Advertise own services 
Lookup for resourc...
OOnnttoonnuuttss aass aa ttooooll ffoorr sseemmaannttiicc iinntteeggrraattiioonn
Ontonuts: Competence Profile ooff aann AAggeenntt aass aa 
sseerrvviiccee pprroovviiddeerr ((““wwhhaatt ccaann II ddoo”” a...
External vviieeww ttoo oonnttoonnuuttss:: SShhaarreedd 
CCoommppeetteennccee SSppeecciiffiiccaattiioonn 
You 
can 
ask me ...
Internal view to oonnttoonnuuttss:: AAccttiioonn oorr QQuueerryy 
PPllaannss 
You 
can 
ask me 
for … 
External 
Internal ...
Possible ggeenneerraall rruullee ooff oonnttoonnuutt aappppeeaarraannccee 
You 
can 
ask me 
for … 
External 
Internal 
IF...
EExxaammppllee ((11)):: AAttoommiicc OOnnttoonnuutt ##11 
I can answer 
any queries on 
mental diseases 
of citizens of X ...
EExxaammppllee ((22)):: AAttoommiicc OOnnttoonnuutt ##22 
I can answer 
any queries on 
loans in Nordea 
bank 
Nordea 
XML...
EExxaammppllee ((33)):: CCoommpplleexx OOnnttoonnuutt ##33 
I can answer any queries on 
mental diseases and loans of 
Nor...
Industrial RReessoouurrccee LLiiffeeccyyccllee aanndd HHiissttoorryy 
Condition 
Monitoring 
States Symptoms 
RRDDFF 
Meas...
55.. SSeemmaannttiicc PPeerrssoonnaalliizzaattiioonn
Multimeetmobile Project (2000-2001) 
Academy of Finland 
Project (1999): 
Dynamic Integration of 
Classification Algorithm...
CCoonntteexxttuuaall aanndd PPrreeddiiccttiivvee AAttttrriibbuutteess 
 
Contextual 
attributes 
< > im - 
im 
l...
Simple distance bbeettwweeeenn TTwwoo PPrreeffeerreenncceess wwiitthh 
HHeetteerrooggeenneeoouuss AAttttrriibbuutteess ((E...
Advanced distance between Two 
Preferences with Heterogeneous Attributes 
(Example) - 1 
64 
å 
( , ) w ( , )2 
D X Y = × ...
Advanced distance between Two 
Preferences with Heterogeneous Attributes 
(Example) - 2 
65 
å 
( , ) w ( , )2 
D X Y = × ...
PPrreeddiiccttiioonn ooff CCuussttoommeerr’’ss AAccttiioonnss 
d1 d2 
d3 
d4 
d5 
here I washed my car 
here I had nice wi...
SSmmaarrtt aassssiissttaanntt 
Preferences 
(semantic profile) 
Advices based on automatically 
Assumtion: 
”users like wh...
SSmmaarrtt aassssiissttaanntt 
Advices based on configured 
preferences 
I like it 
…saving to the history… 
food ordered ...
66.. SSeemmaannttiicc PPrrooaaccttiivviittyy
IInntteelllliiggeenntt aannsswweerriinngg mmaacchhiinnee 
Sometimes users 
cannot answer the 
income call 
Away for sports...
IInntteelllliiggeenntt aannsswweerriinngg mmaacchhiinnee 
A phone was 
lost or stolen …however they can 
Sometimes users 
...
GGUUNN CCoonncceepptt:: AAllll GGUUNN rreessoouurrcceess ““uunnddeerrssttaanndd”” eeaacchh ootthheerr 
Real 
World 
object...
WWeebb SSeerrvviicceess ffoorr SSmmaarrtt DDeevviicceess 
Smart industrial devices can be 
also Web Service “users”. Their...
Global Network ooff MMaaiinntteennaannccee SSeerrvviicceess 
OntoServ.Net: “Semantic Web Enabled Network of Maintenance 
S...
SSmmaarrtt MMaaiinntteennaannccee EEnnvviirroonnmmeenntt 
““EExxppeerrttss iinn eennvviirroonnmmeennttaall 
On-line learni...
WWhhaatt iiss UUBBIIWWAARREE ((iinn sshhoorrtt)) 
• UUBBIIWWAARREE iiss aa ttooooll ttoo ssuuppppoorrtt:: 
 ddeessiiggnn ...
CCuurrrreenntt UUBBIIWWAARREE AAggeenntt AArrcchhiitteeccttuurree 
SS--AAPPLL – 
Semantic 
Agent 
Programming 
Language 
(...
KKeeyy CCoommppoonneennttss ooff UUBBIIWWAARREE 
SScciieennttiiffiicc IImmppaacctt 
33.. LLaanngguuaaggee 
11.. UUBBIIWWAA...
Semantic Integration in UBIWARE 3.0
Presentation Case for UBIWARE 3.0
X1 :firstName :Vagan 
X1 :lastName :Terziyan 
X1 :sex :Male 
X1 :birthday :27/12/1958 
X1 :email 
:vagan@it.jyu.fi 
X1 :in...
UUBBIIWWAARREE AAggeenntt:: PPoossssiibbllee FFuuttuurree AArrcchhiitteeccttuurree 
RRBBEE – 
Reusable 
Behavior 
Engine R...
77.. SSeemmaannttiicc VViissuuaalliizzaattiioonn
TThhiiss iiss nnoott ssiimmppllee 
Cube (ID1) 
Ball (ID2) 
Table (ID3) 
hasColor (ID1, “Green”) 
hasColor (ID2, “Red”) 
ha...
SSeemmaannttiicc MMaasshh--UUpp eennggiinnee 
needs also context-based 
relevant 
features selection 
“In the idea of a se...
44ii ((““ffoorr eeyyee””)) SSeemmaannttiiccaallllyy eennhhaanncceedd ccoonntteexxtt--bbaasseedd mmuullttiiddiimmeennssiioo...
EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp
EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp 
115 
Terziyan V., Kaykova O., ...
EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp 
University of Jyvaskyla 
On-th...
88.. SSeemmaannttiiccss--EEnnaabblleedd GGaammeess
MMeettaaGGaammee:: sseemmaannttiiccaallllyy aannnnoottaatteedd eeppiissooddeess 
Semantical Games Space 
Semantic Match 
M...
Unified GGaammee PPrrooffiillee ooff aa PPllaayyeerr 
• Saved game data (game state, user level, points, etc.) can be shar...
PPeerrssoonnaalliizzaattiioonn ooff ggaammeess 
Games can be designed in a way that they have standardized 
(semantic) des...
EEdduuccaattiioonn SSuuppppoorrtt GGaammeess 
GGoo ttoo tthhee nneexxtt 
Exercise 
storage 
Game 
Assistant 
Home Exercise...
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Semantic Web: Applications

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Semantic Annotation
Semantic Communication
Semantic Search
Semantic Integration
Semantic Personalization
Semantic Proactivity
Semantic Visualization
Semantic Games

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Semantic Web: Applications

  1. 1. SSeemmaannttiicc WWeebb:: AApppplliiccaattiioonnss Ch Aatif Hussain Warraich
  2. 2. CCoonntteenntt 1. Semantic Annotation 2. Semantic Communication 3. Semantic Search 4. Semantic Integration 5. Semantic Personalization 6. Semantic Proactivity 7. Semantic Visualization 8. Semantic Games
  3. 3. Technology RRooaaddmmaapp ffoorr AApppplliiccaattiioonnss Semantic Search 2 P2P Agent Technology Web Services Semantic Web (SW) Semantic Integration Semantic Games Semantic Proactivity Semantic Personalization Machine Learning Semantic Communication Semantic Annotation 1 3 4 5 6 7
  4. 4. 11.. SSeemmaannttiicc aannnnoottaattiioonn
  5. 5. OOnnttoollooggyy--bbaasseedd UUsseerr IInntteerrffaaccee SSiimmppllee uusseerr ddaattaa oonnttoollooggyy ffoorr mmoobbiillee pphhoonneess Model of user’s data and other resources: - Contacts (phone numbers, names etc.) - Notes (some pieces of text) - Calendar (with some events assigned) Auto-generated form for data Data to store in every instance of defined information model
  6. 6. UUssiinngg ggeenneerraatteedd iinntteerrffaaccee For described data model forms are generated 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!
  7. 7. Access yyoouurr ddaattaa qquuiicckkllyy aanndd eeaassiillyy…… 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 of contacts
  8. 8. Browsing tthhee aannnnoottaatteedd ddaattaa
  9. 9. Using image mmeettaaddaattaa ffoorr bbrroowwssiinngg aanndd lliinnkkiinngg ttoo ootthheerr ddaattaa WWoorrkksshhoopp 1122//0044//22000033 Oleksiy VVaaggaann – IIOOGG Khriyenko TTeerrzziiyyaann && MMeettssoo FFiinnllaanndd,, JJyyvväässkkyyllää IInnffoorrmmaattiioonn:: …… LLiinnkkeedd ttoo:: <<iimmaaggee:: VVaaggaann TTeerrzziiyyaann>> WWoorrkksshhoopp – IIOOGG && MMeettssoo 1122//0044//22000033 FFiinnllaanndd,, JJyyvväässkkyyllää IInnffoorrmmaattiioonn:: …… 1122//0044//220033 FFiinnllaanndd,, JJyyvväässkkyyllää IInnffoorrmmaattiioonn:: …… PPaarrtt ooff <<iimmaaggee:: WWoorrkksshhoopp –– IIOOGG && MMeettssoo>> LLiinnkk ttoo <<OOlleekkssiiyy KKhhrriiyyeennkkoo>> SSeelleecctt iimmaaggeess bbyy:: 1122//0044//220033 FFiinnllaanndd,, JJyyvväässkkyyllää IInnffoorrmmaattiioonn:: …… PPaarrtt ooff <<iimmaaggee:: WWoorrkksshhoopp –– IIOOGG && MMeettssoo>> LLiinnkk ttoo <<VVaaggaann TTeerrzziiyyaann>> <<iimmaaggee:: JJoouunnii PPyyööttssiiää>> <<iimmaaggee:: OOlleekkssiiyy KKhhrriiyyeennkkoo>> <<iimmaaggee:: AAnnddrriiyy ZZhhaarrkkoo>> <<iimmaaggee:: OOlleekkssaannddrr KKoonnoonneennkkoo>> NNaammee:: VVaaggaann TTeerrzziiyyaann SSeexx:: MMaallee DDaattee ooff BBiirrtthh:: 2277 DDeecceemmbbeerr,, 11995588 CCiittiizzeennsshhiipp:: UUkkrraaiinnee <<OOlleekkssiiyy KKhhrriiyyeennkkoo>> PPhhoonnee:: ++335588 1144 226600 33001111 EE--mmaaiill:: vvaaggaann@@iitt..jjyyuu..ffii UURRLL:: wwwwww..ccss..jjyyuu..ffii//aaii//vvaaggaann …… -- DDaattee -- LLiinnkk:: -- PPllaaccee ((llooccaattiioonn)) -- …… …… ……
  10. 10. Location bbaasseedd iimmaaggee aannnnoottaattiioonn 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>
  11. 11. Location bbaasseedd PPhhoottoo AAllbbuumm--MMaapp FFiinnllaanndd JJyyvväässkkyyllää Finland Jyväsky lä Agora FFiinnllaanndd JJyyvväässkkyyllää AAggoorraa 1133//0088//22000033 IInnffoorrmmaattiioonn:: …… MMaakkee aa iimmaaggee ttrriipp mmaapp:: -- ddaayy -- mmoonntthh -- yyeeaarr …… …… NNookkiiaa 1133//0088//22000033
  12. 12. CCoommppoossiinngg PPhhoottoo AAllbbuummss uussiinngg mmeettaaddaattaa USER 1 USER 2 USER N Web server “My Friends” “Wedding” “Workgroup” “Our Holidays”
  13. 13. BBAANNKK:: DDaattaa aannnnoottaattiioonn 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 of some 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 Annotated data (RDF) Processing of data by some other semantic-aware applications
  14. 14. 22.. SSeemmaannttiicc CCoommmmuunniiccaattiioonn
  15. 15. SSeemmaannttiicc CCaallll Call to a person, who can satisfy my needs/requirements. Needs: Buy Car what model BMW 318i NO Addresses 1995 - … age <= 250000 mileage <= 7500 e price my location Finland my location SEMA – semantic profile based matching service Call to a person, who can satisfy my needs/requirements. Needs: Sell Car what model BMW 318i age 1998 150000 mileage 7000 e price Finland my location Needs: Sell Car what model BMW 318i age 1998 mileage 150000 7000 e price Finland my location Needs: Buy Car what model BMW 318i 1995 - … age <= 250000 mileage <= 7500 e price Finland Semantic Match of the Profiles High Level of Privacy. IDs Phone Numbers Interests Profile JUST Business
  16. 16. SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((hhuummaann--ttoo--hhuummaann)) uusseerr request for semantic call Search agent, provides “semantic match” functionality Shared ontology Semantic annotation users
  17. 17. SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((hhuummaann--ttoo--mmaacchhiinnee)) request for semantic call Search agent, provides “semantic match” functionality Shared ontology Condition Monitoring Expert Semantic annotation Field devices
  18. 18. SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((mmaacchhiinnee--ttoo--hhuummaann)) request for semantic call Search agent, provides “semantic match” functionality Shared ontology SSmmaarrtt ddeevviiccee Semantic annotation Fault diagnostics experts
  19. 19. SSeemmaannttiicc CCoommmmuunniiccaattiioonn ((mmaacchhiinnee--ttoo--mmaacchhiinnee)) request for semantic call Search agent, provides “semantic match” functionality Shared ontology SSmmaarrtt ddeevviiccee Semantic annotation Field devices
  20. 20. SSeemmaannttiicc CCaallll • 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.
  21. 21. Clients Public merchants, AArrcchhiitteeccttuurree ffoorr aa MMoobbiillee PP--CCoommmmeerrccee SSeerrvviiccee public customers, public information providers … … 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 External Environment … Map Content Providers Server Location Providers Server … Content Providers Server … RDF $ $ $ Banks 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.
  22. 22. 33.. SSeemmaannttiicc SSeeaarrcchh
  23. 23. SSeemmaannttiicc WWeebb:: SSeemmaannttiicc SSeeaarrcchh uusseerr request for semantic search Shared ontology Semantic annotation Web resources / services / DBs / etc. Search agent, provides “semantic match” functionality
  24. 24. SSeemmaannttiicc SSeeaarrcchh 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.
  25. 25. SSeemmaannttiicc FFaacciilliittaattoorrss ffoorr WWeebb IInnffoorrmmaattiioonn RReettrriieevvaall ((22000044)) IInnBBCCTT TTeekkeess PPRROOJJEECCTT CChhaapptteerr 33..11..33 :: ““IInndduussttrriiaall OOnnttoollooggiieess aanndd SSeemmaannttiicc WWeebb”” ((yyeeaarr 22000044)) 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.
  26. 26. HHooww ddooeess iitt wwoorrkk?? 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
  27. 27. SSeennssee DDeetteerrmmiinnaattiioonn • WWoorrddNNeett 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
  28. 28. SSeemmaannttiicc SSeeaarrcchh AAssssiissttaanntt
  29. 29. HHooww ddooeess iitt wwoorrkk?? 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
  30. 30. OOnnttoollooggyy PPeerrssoonnaalliizzaattiioonn:: iiss mmeecchhaanniissmm,, wwhhiicchh aalllloowwss uusseerrss ttoo hhaavvee oowwnn ccoonncceeppttuuaall vviieeww aanndd bbee aabbllee ttoo uussee iitt ffoorr sseemmaannttiicc qquueerryyiinngg ooff sseeaarrcchh ffaacciilliittiieess.. “Driver” “Driver” “Driver” “Driver” “Driver” CCoommmmoonn oonnttoollooggyy SSeeaarrcchh OOnnttoollooggyy PPeerrssoonnaalliizzaattiioonn
  31. 31. WWoorrddNNeett 22..00 SSeeaarrcchh EExxaammppllee • 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 1 driver -- (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 automo bile) => owner-driver -- (a motorist who owns the car that he/she drives) => racer, race driver, automobile driver -- (someone who drives racing car s at
  32. 32. GGeenneerraattiinngg ooff rreeqquueessttss sseett • 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
  33. 33. SSeemmaannttiicc SSeeaarrcchh EEnnhhaanncceemmeenntt :: CCoommmmoonn ((lliinngguuiissttiicc)) oonnttoollooggyy DDoommaaiinn oonnttoollooggyy QQuueerryy :: XX XX XX XX XX XX (( XX XX XX )) XX SSeemmaannttiiccFFiilltteerriinngg RReessuulltt:: EEnnaabblliinngg tthhee SSeemmaannttiicc SSeeaarrcchh SSeemmaannttiicc SSeeaarrcchh AAssssiissttaanntt ((FFaacciilliittaattoorr)) uusseess oonnttoollooggiiccaallllyy ((WWoorrddNNeett)) ddeeffiinneedd kknnoowwlleeddggee aabboouutt wwoorrddss aanndd eemmbbeeddddeedd ssuuppppoorrtt ooff aaddvvaanncceedd GGooooggllee--sseeaarrcchh qquueerryy ffeeaattuurreess iinn oorrddeerr ttoo ccoonnssttrruucctt mmoorree eeffffiicciieenntt qquueerriieess ffrroomm ffoorrmmaall tteexxttuuaall ddeessccrriippttiioonn ooff sseeaarrcchheedd iinnffoorrmmaattiioonn.. SSeemmaannttiicc SSeeaarrcchh AAssssiissttaanntt hhiiddeess ffrroomm uusseerrss tthhee ccoommpplleexxiittyy ooff qquueerryy llaanngguuaaggee ooff ccoonnccrreettee sseeaarrcchh eennggiinnee aanndd ppeerrffoorrmmss rroouuttiinnee aaccttiioonnss tthhaatt mmoosstt ooff uusseerrss ddoo iinn oorrddeerr ttoo aacchhiieevvee bbeetttteerr ppeerrffoorrmmaannccee aanndd ggeett mmoorree rreelleevvaanntt rreessuullttss..
  34. 34. EExxaammppllee • 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)
  35. 35. SSeemmaannttiicc SSeeaarrcchh ooff PPeeooppllee 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).
  36. 36. 44.. SSeemmaannttiicc IInntteeggrraattiioonn
  37. 37. SSeemmaannttiicc WWeebb:: SSeemmaannttiicc IInntteeggrraattiioonn Integrated resource Shared ontology Semantic annotation Web resources / services / DBs / etc.
  38. 38. IInntteeggrraatteedd ddooccuummeenntt:: SSmmaarrttMMeessssaaggee Message for John 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> <tag> 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> UURRIIss Resource description via personal ontology … “Mood” of the message ? recipient – “John” wife sender house sender address Roninmäentie 5T 23 today at 20:00. Our address is Roninmäentie 5T 23 sender address map sender family Semantic Search Message from Michele <##URI> 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> OObbjjeeccttss recipient wife name – “July” Sender - Michele Sender - Michele Sender - Michele Sender - Michele ?? house address address map family ?? ?? ?? ?? Semantic Search SMS Request for objects MMS (objects) Message from Michele Invite you and July to celebrate house-warming today at 20:00. Our address is Roninmäentie 5T 23 With the best regards, Crawford family Message from Michele Invite you and July to celebrate house-warming With the best regards, Crawford family
  39. 39. CCoorrppoorraattee//BBuussiinneessss HHuubb Publish own resource descriptions Advertise own services Lookup for resources with semantic search Hub ontology and shared domain ontologies Companies would be able to create “Corporate Hubs”, which would be an excellent cooperative business environment for their applications. Software and data reuse Automated access to enterprise (or partners’) resources Seamless integration of services Partners / Businesses What parties can do: What parties achieve: Ontologies will help to glue such Enterprise-wide / Cooperative Semantic Web of shared resources
  40. 40. OOnnttoonnuuttss aass aa ttooooll ffoorr sseemmaannttiicc iinntteeggrraattiioonn
  41. 41. Ontonuts: Competence Profile ooff aann AAggeenntt aass aa sseerrvviiccee pprroovviiddeerr ((““wwhhaatt ccaann II ddoo”” aanndd ““wwhhaatt ccaann II aannsswweerr””)) aanndd aapppprroopprriiaattee sseerrvviiccee ppllaann ((““hhooww II ddoo …… oorr aannsswweerr ……””)) You can ask me for … a) … action b) … information ontonut
  42. 42. External vviieeww ttoo oonnttoonnuuttss:: SShhaarreedd CCoommppeetteennccee SSppeecciiffiiccaattiioonn You can ask me for … a) I know everything about Mary b) I know everything about cats c) I know what time it is now d) I know all lovers of John e) I know grades on chemistry of all pupils from 4-B a) I can open the door #456 b) I can fly c) I can use knifes d) I can build house from wood e) I can visualize maps f) I can grant access to folder “444” We consider ONTONUTS to be shared S-APL specifications of these competences External Internal
  43. 43. Internal view to oonnttoonnuuttss:: AAccttiioonn oorr QQuueerryy PPllaannss You can ask me for … External Internal 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
  44. 44. Possible ggeenneerraall rruullee ooff oonnttoonnuutt aappppeeaarraannccee You can ask me for … External Internal IF I have the plan how to perform certain complex or simple action or the plan how to answer complex or simple query AND {time-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 will create ONTONUT which will make my competence on this plan explicit and visible to others
  45. 45. EExxaammppllee ((11)):: AAttoommiicc OOnnttoonnuutt ##11 I can answer any queries on mental diseases of citizens of X City X Central Hospital Relational Database Give me the list of women from X with mental diseases diagnosed after I know how appropriate database is organized, I have access rights and I am able to query it 2006
  46. 46. EExxaammppllee ((22)):: AAttoommiicc OOnnttoonnuutt ##22 I can answer any queries on loans in Nordea bank Nordea XML Database Give me the list of Nordea clients with loans of more than 100 000 EURO I know how appropriate database is organized, I have access rights and I am able to query it
  47. 47. EExxaammppllee ((33)):: CCoommpplleexx OOnnttoonnuutt ##33 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
  48. 48. Industrial RReessoouurrccee LLiiffeeccyyccllee aanndd HHiissttoorryy Condition Monitoring States Symptoms RRDDFF Measurement Data Warehousing Predictive Measureme nt Fault detection, alarms Diagnostics Predictive Monitorin g HHiissttoorryy e Plan Warehousin RRDDFF Predictive Diagnostics Maintenance Fault localization isolation Maintenance Planning Conditions Warehousin g Industrial Resource Predictive Maintenanc g Diagnoses Warehousin g Fault identification, Maintenance Plan Diagnoses
  49. 49. 55.. SSeemmaannttiicc PPeerrssoonnaalliizzaattiioonn
  50. 50. Multimeetmobile Project (2000-2001) Academy of Finland Project (1999): Dynamic Integration of Classification Algorithms Mobile LLooccaattiioonn--BBaasseedd SSeerrvviiccee iinn 18 Information Technology Research Institute (University of Jyvaskyla): Customer-oriented research and development in Information Technology http://www.titu.jyu.fi/eindex.html Multimeetmobile (MMM) Project (2000-2001): Location-Based Service System and Transaction Management in Mobile Electronic Commerce http://www.cs.jyu.fi/~mmm SSeemmaannttiicc WWeebb 19 M-Commerce LBS system http://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-based Service system (MLS), has been developed. MLS is a general LBS system for mobile users, offering map and navigation across multiple geographically distributed services accompanied with access to location-based information through the map on terminal’s screen. MLS is based on Java, XML and uses dynamic selection of services for customers based on their profile and location. Virrantaus K., Veijalainen J., Markkula J., Katasonov A., Garmash A., Tirri H., Terziyan V., Developing GIS-Supported Location-Based Services, In: Proceedings of WGIS 2001 - First International Workshop on Web Geographical Information Systems, 3-6 December, 2001, Kyoto, Japan, pp. 423-432. 20 Adaptive interface for MLS client Only predicted services, for the customer with known profile and location, will be delivered from MLS and displayed at the mobile terminal screen as clickable “points of interest” 21 Route-based personalization Inductive learning of customer preferences with integration of predictors Sample Instances < xr1, xr2 ,..., xrm ® yr > < xt1, xt2,..., xtm > Learning Environment Predictors/Classifiers P1 P2 ... Pn yt Terziyan V., Dynamic Integration of Virtual Predictors, In: L.I. Kuncheva, F. Steimann, C. Haefke, M. Aladjem, V. Novak (Eds), Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications - CIMA'2001, Bangor, Wales, UK, June 19 - 22, 2001, ICSC Academic Press, Canada/The Netherlands, pp. 463-469. Static Perspective Dynamic Perspective 22
  51. 51. CCoonntteexxttuuaall aanndd PPrreeddiiccttiivvee AAttttrriibbuutteess  Contextual attributes < > im - im location features profile features i i x x x x 1 1 2 , , ......, , yi Mobile customer description Ordered service Predictive attributes
  52. 52. Simple distance bbeettwweeeenn TTwwoo PPrreeffeerreenncceess wwiitthh HHeetteerrooggeenneeoouuss AAttttrriibbuutteess ((EExxaammppllee)) å " Î Î ( , ) w ( , )2 D X Y = × d x y i i i i , x X , y Y i i where : ì ï ï í ï ï î x - y 0, if î í ì = - = i i i i i i i range x y i d x y else : 1, otherwise if th attribute is nominal - ( , ) 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 (“white”, “red”) = 1 d (15°, 25°) = 10°/((+30°)-(+10°)) = 0.5 D (Wine_preference_1, Wine_preference_2) = √ (0.7• 1 + 0.3 • 0.5) ≈ 0.922
  53. 53. Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 1 64 å ( , ) w ( , )2 D X Y = × d x y i i i i x X y Y " Î Î , , i i where : ì ï ï C c ( , ) 1 í ï ï î 2 P c x P c y i - - å= [ ( | ) ( | )] ,if th attribute is nominal; x y i i - - = | | i i ,if i th attribute is numerical. range d x y i i i P(wine|colour = white) = = 100 / 500 = 0.2 P(wine|colour = red) = = 200 / 300 = 0.67 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
  54. 54. Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 2 65 å ( , ) w ( , )2 D X Y = × d x y i i i i x X y Y " Î Î , , i i where : P(wine|colour = white) = = 100 / 500 = 0.2 P(wine|colour = red) = = 200 / 300 = 0.67 ì ï ï ( , ) 1 í ï ï î 2 P c x P c y i - - [ ( | ) ( | )] ,if th attribute is nominal; x y - - = å= | i i | ,if i th attribute is numerical. range d x y i C c i i i i P(soft_drink|colour = white) = = 400 / 500 = 0.8 P(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 ] = D ( = √ [(0.8 – 0.33 )2 + (0.2 – 0.67 )2 ] ≈ 0.665 Wine_preference_1, Wine_preference_2) = √ (0.7• 0.665 + 0.3 • 0.5) ≈ 0.784
  55. 55. PPrreeddiiccttiioonn ooff CCuussttoommeerr’’ss AAccttiioonnss 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: 0.1 2. I want to drink some wine: 0.2 3. I need a massage: 0.2 4. I want to eat pizza: 0.8 5. I need to make my hair: 0.6 Make a guess what I will order now and where !
  56. 56. SSmmaarrtt aassssiissttaanntt Preferences (semantic profile) Advices based on automatically Assumtion: ”users like what they photograph” Somewhere in the other places Nearby there is a wounderful old castle! collected preferences (photographing) Conclusion: ”users likes old castles” location-based annotations location awareness
  57. 57. SSmmaarrtt aassssiissttaanntt Advices based on configured preferences I like it …saving to the history… food ordered through mobile phone 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
  58. 58. 66.. SSeemmaannttiicc PPrrooaaccttiivviittyy
  59. 59. IInntteelllliiggeenntt aannsswweerriinngg mmaacchhiinnee Sometimes users cannot answer the income call Away for sports Visiting important meeting Making presentation or lecturing Studying A phone was lost or stolen Sleeping
  60. 60. IInntteelllliiggeenntt aannsswweerriinngg mmaacchhiinnee A phone was lost or stolen …however they can Sometimes users cannot answer the income call Away for sports Visiting important meeting Making presentation or lecturing camera phone location data Studying wake up! I need you today! schedule data Sleeping 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. location data camera 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.
  61. 61. GGUUNN CCoonncceepptt:: AAllll GGUUNN rreessoouurrcceess ““uunnddeerrssttaanndd”” eeaacchh ootthheerr Real World objects Real World Object + + OntoAdapter + + OntoShell = = GGUUNN RReessoouurrccee OntoAdapters GGUUNN OntoShells Real World objects of new generation (OntoAdapter inside)
  62. 62. WWeebb SSeerrvviicceess ffoorr SSmmaarrtt DDeevviicceess 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,
  63. 63. Global Network ooff MMaaiinntteennaannccee SSeerrvviicceess OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, March 2003,
  64. 64. SSmmaarrtt MMaaiinntteennaannccee EEnnvviirroonnmmeenntt ““EExxppeerrttss iinn eennvviirroonnmmeennttaall On-line learning ““SSttaaffff//ssttuuddeennttss with monitored organizational data”” ““DDeevviicceess wwiitthh oonn--lliinnee ddaattaa”” ““MMaannaaggeerr//EExxppeerrtt”” ““EExxppeerrttss”” exchang Maintenance data ee Maintenance data exchange mmoonniittoorriinngg”” ““SSeerrvviicceess”” ““Human/patient with embedded medical sensors ”” ““DDooccttoorr//EExxppeerrtt”” ““MMeeddiiccaall WWeebb ““WWeebb SSeerrvviicceess ffoorr eennvviirroonnmmeeSSnnetetaarrllv viicceess”” ddiiaaggnnoossttiiccss aanndd pprreeddiiccttiioonn”” ““Environment with sensors ”” ““WWeebb SSeerrvviicceess iinn oorrggaanniizzaattiioonnaall ddiiaaggnnoossttiiccss aanndd mmaannaaggeemmeenntt””
  65. 65. WWhhaatt iiss UUBBIIWWAARREE ((iinn sshhoorrtt)) • UUBBIIWWAARREE iiss aa ttooooll ttoo ssuuppppoorrtt::  ddeessiiggnn aanndd iinnssttaallllaattiioonn ooff……,,  aauuttoonnoommiicc ooppeerraattiioonn ooff…… aanndd  iinntteerrooppeerraabbiilliittyy aammoonngg…… • …… ccoommpplleexx,, hheetteerrooggeenneeoouuss,, ooppeenn,, ddyynnaammiicc aanndd sseellff--ccoonnffiigguurraabbllee ddiissttrriibbuutteedd iinndduussttrriiaall ssyysstteemmss;;…… • …… aanndd ttoo pprroovviiddee ffoolllloowwiinngg sseerrvviicceess ffoorr ssyysstteemm ccoommppoonneennttss::  aaddaappttaattiioonn;;  aauuttoommaattiioonn;;  cceennttrraalliizzeedd oorr PP22PP oorrggaanniizzaattiioonn;;  ccoooorrddiinnaattiioonn,, ccoollllaabboorraattiioonn,, iinntteerrooppeerraabbiilliittyy aanndd nneeggoottiiaattiioonn;;  sseellff--aawwaarreenneessss,, ccoommmmuunniiccaattiioonn aanndd oobbsseerrvvaattiioonn;;  ddaattaa aanndd pprroocceessss iinntteeggrraattiioonn;;  ((sseemmaannttiicc)) ddiissccoovveerryy,, sshhaarriinngg aanndd rreeuussee..
  66. 66. CCuurrrreenntt UUBBIIWWAARREE AAggeenntt AArrcchhiitteeccttuurree SS--AAPPLL – Semantic Agent Programming Language (RDF-based) http://users.jyu.fi/~akataso/sapl.ht ml
  67. 67. KKeeyy CCoommppoonneennttss ooff UUBBIIWWAARREE SScciieennttiiffiicc IImmppaacctt 33.. LLaanngguuaaggee 11.. UUBBIIWWAARREE:: AApppprrooaacchh aanndd AArrcchhiitteeccttuurree 22.. EEnnggiinnee BBuussiinneessss PPrroocceessss CChhoorreeooggrraapphhyy 44.. OOnnttoonnuuttss EExxtteerrnnaall CCaappaabbiilliittiieess OOrrcchheessttrraattiioonn
  68. 68. Semantic Integration in UBIWARE 3.0
  69. 69. Presentation Case for UBIWARE 3.0
  70. 70. X1 :firstName :Vagan X1 :lastName :Terziyan X1 :sex :Male X1 :birthday :27/12/1958 X1 :email :vagan@it.jyu.fi X1 :interest :fishing X1 :hasPhoto #vagan.jpg X1 :group :IOG X1 :group :RuleML … X1 :education :KNURE X1 :position :professor X1 :hasFriend X2 X2 :firstName :Alain X2 :lastName :Gourdin … X1 :hasFriend X3 X3 :firstName :Mikko X3 :lastName :Vapa … Linked Data
  71. 71. UUBBIIWWAARREE AAggeenntt:: PPoossssiibbllee FFuuttuurree AArrcchhiitteeccttuurree RRBBEE – Reusable Behavior Engine RR ““LLiiffee”” BBeehhaavviioorr RR BB EE RR BB EE RR BB EE RR BB EE BBeelliieeffss ((ffaaccttss,, rruulleess,, ppoolliicciieess,, ppllaannss)) SShhaarreedd MMeettaa--BBeelliieeffss SShhaarreedd RRBBEEss EEnnvviirroonnmmeenntt SSooffttSSoouull HHaarrddSSoouull SSooffttMMiinndd HHaarrddMMiinndd SSooffttBBooddyy HHaarrddBBooddyy RRAABB – Reusable Atomic Behavior AA BB RR AA BB RR AA BB RR AA BB SShhaarreedd BBeelliieeffss SShhaarreedd RRAABBss MMeettaa--BBeelliieeffss ((pprreeffeerreenncceess)) CCoonnffiigguurraattiioonn ((GGEENNOOMMEE)) SShhaarreedd HHaarrddwwaarree “Visible” to other agents through observation OOnnttoobbiilliittyy 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 uGGtieelitnny-oobammseeed ibse phaarvti oorf semantically marked-up agent configuration settings, which can serve as a tool for agent evolution: inheritance crossover and mutation May be an agent
  72. 72. 77.. SSeemmaannttiicc VViissuuaalliizzaattiioonn
  73. 73. TThhiiss iiss nnoott ssiimmppllee Cube (ID1) Ball (ID2) Table (ID3) hasColor (ID1, “Green”) hasColor (ID2, “Red”) hasColor (ID3, “Brown”) isOnTheLeftSideOf (ID2, ID1) hasTemperatureC (ID1, 30) hasTemperatureC (ID2, 25) isOn (ID1, ID3) isOn (ID2, ID3) isLarger (ID2, ID1) 30° 25°
  74. 74. SSeemmaannttiicc MMaasshh--UUpp eennggiinnee needs also context-based relevant features selection “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
  75. 75. 44ii ((““ffoorr eeyyee””)) SSeemmaannttiiccaallllyy eennhhaanncceedd ccoonntteexxtt--bbaasseedd mmuullttiiddiimmeennssiioonnaall RReessoouurrccee VViissuuaalliizzaattiioonn ((OO.. KKhhrriiyyeennkkoo)) 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. Healthcare Person-location based PPeerrssoonn Organs’ condition Employer based Location of healthcare organization Work-place location Work Members, training facilities (stadium) Training teams, football field, … Occupation, profession FFoooottbbaallll tteeaamm SSttaaddiiuumm PPeerrssoonn HHuummaann hheeaarrtt PPeerrssoonn Family relation Family relation Internal Condition Consisting of external systems Work Occupation, profession Treatment of cardiovascular disease Medical center location “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.
  76. 76. EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp
  77. 77. EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp 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.
  78. 78. EExxeeccuuttaabbllee RReeaalliittyy:: EExxeeccuuttaabbllee sseemmaannttiicc mmeesshh--uupp University of Jyvaskyla On-the-fly generated statistics Executable Focus 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.
  79. 79. 88.. SSeemmaannttiiccss--EEnnaabblleedd GGaammeess
  80. 80. MMeettaaGGaammee:: sseemmaannttiiccaallllyy aannnnoottaatteedd eeppiissooddeess Semantical Games Space Semantic Match MMeettaaGGaammee On-line Semantic Composition of the Games
  81. 81. Unified GGaammee PPrrooffiillee ooff aa PPllaayyeerr • 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 storage Game Profile ontologies
  82. 82. PPeerrssoonnaalliizzaattiioonn ooff ggaammeess 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
  83. 83. EEdduuccaattiioonn SSuuppppoorrtt GGaammeess GGoo ttoo tthhee nneexxtt Exercise storage Game Assistant Home Exercise Home Exercise Home Exercise Home Exercise lleevveell YYoouu sshhoouulldd mmaakkee ssoommee eexxeerrcciisseess 55 ++ 2233 == ?? 113311 –– 9944 == ?? 22 ** 55 == ?? History Mathematics Geography Biology

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