Integra(ng	
  Social	
  Web	
  &	
  TV	
  
   	
  with	
  help	
  of	
  seman(cs	
  
 Lora	
  Aroyo	
  &	
  Guus	
  Schrei...
Seman(c	
  Web	
  @	
  VU	
  Amsterdam	
  
•  40	
  people,	
  two	
  groups:	
  Web	
  &	
  Media	
  (Schreiber),	
  
   ...
The	
  Linked	
  Data	
  Web:	
  	
  
    typed	
  resources	
  and	
  links	
  
Painting          Dublin Core        ULAN...
Demo	
  using	
  linked	
  data	
  (RPI,	
  Hendler)	
  
The	
  power	
  of	
  simple	
  alignments	
  
     “Tokugawa”




 AAT style/period                SVCN
  Edo (Japanese p...
hVp://e-­‐culture.mul(median.nl/demo/search	
  	
  
Europeana	
  Thought	
  Lab	
  cloud	
  
From	
  metadata	
  to	
  	
  
           seman(c	
  metadata	
  

      SKOS                              EDM
1. Make voc...
Enriching	
  the	
  metadata	
  
Resul(ng	
  seman(c	
  annota(on	
  	
  
Personalized	
  Rijksmuseum	
  




                                  http://chip-project.org
        NoTube 1st review
Personalized	
  Rijksmuseum	
  




                                        http://chip-project.org
05-06 May     NoTube 1...
Mobile	
  Museum	
  Tour	
  




http://www.chip-project.org/demo/mobileguide/index.jsp
Crowdsourcing:	
  Video	
  Tagging	
  Games	
  




                                          http://waisda.nl
NoTube:	
  	
  
   Making	
  Television	
  More	
  Personal	
  




hVp://www.notube.tv	
  	
  
Acronym	
  and	
  consor(um	
  
NoTube	
  slogan:	
  
      Pu#ng	
  the	
  user	
  back	
  in	
  the	
  driving	
  seat	
  

Observa(ons:	
  	
  
•  Pers...
NoTube	
  building	
  blocks	
  (1)	
  
1.	
  TV	
  metadata	
  services	
  
    EPG	
  metadata	
  grabbers	
  	
  
    –...
NoTube	
  building	
  blocks	
  (2)	
  
3.	
  Linked	
  Open	
  Data	
  for	
  TV	
  
     –  Access	
  services	
  to	
  ...
NoTube	
  building	
  blocks	
  (3)
                                           	
  
5.	
  User	
  profiling	
  
    Service...
Usage	
  scenario	
  &	
  demo	
  
http://notube.tv



                              Thank	
  you	
  
                              Ques(ons?	
  




A world...
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
From Enriched Museum Collections to Social Web and TV: Seminar at BBC
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From Enriched Museum Collections to Social Web and TV: Seminar at BBC

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Presentaiton by Lora Aroyo and Guus Schreiber
July 7, 2010

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From Enriched Museum Collections to Social Web and TV: Seminar at BBC

  1. 1. Integra(ng  Social  Web  &  TV    with  help  of  seman(cs   Lora  Aroyo  &  Guus  Schreiber   Computer  Science   VU  University  Amsterdam  
  2. 2. Seman(c  Web  @  VU  Amsterdam   •  40  people,  two  groups:  Web  &  Media  (Schreiber),   Knowledge  Representa(on  &  Reasoning  (van   Harmelen)   •  A  few  ongoing  projects:   –  europeana.eu:  EU  culture  portal   –  NL  projects  on  access  to  cultural  heritage:  CHIP,  Agora   –  EU  NoTube:  Web  &  TV  seman(c  integra(on   –  PrestoPrime:  user-­‐generated  annota(ons  and  content  for   TV  archives   –  EU  LarKC:  plaRorm  for  massive  distributed  reasoning  
  3. 3. The  Linked  Data  Web:     typed  resources  and  links   Painting Dublin Core ULAN “Woman with hat SFMOMA creator Henri Matisse Web link URL URL
  4. 4. Demo  using  linked  data  (RPI,  Hendler)  
  5. 5. The  power  of  simple  alignments   “Tokugawa” AAT style/period SVCN Edo (Japanese period) period Tokugawa Edo AAT is Getty’s SVCN is local in-house Art & Architecture Thesaurus ethnology thesaurus
  6. 6. hVp://e-­‐culture.mul(median.nl/demo/search    
  7. 7. Europeana  Thought  Lab  cloud  
  8. 8. From  metadata  to     seman(c  metadata   SKOS EDM 1. Make vocabulary 2. Align metadata interoperable schema 4. Align vocabulary 3. Enrich metadata
  9. 9. Enriching  the  metadata  
  10. 10. Resul(ng  seman(c  annota(on    
  11. 11. Personalized  Rijksmuseum   http://chip-project.org NoTube 1st review
  12. 12. Personalized  Rijksmuseum   http://chip-project.org 05-06 May NoTube 1st review 15 2010
  13. 13. Mobile  Museum  Tour   http://www.chip-project.org/demo/mobileguide/index.jsp
  14. 14. Crowdsourcing:  Video  Tagging  Games   http://waisda.nl
  15. 15. NoTube:     Making  Television  More  Personal   hVp://www.notube.tv    
  16. 16. Acronym  and  consor(um  
  17. 17. NoTube  slogan:   Pu#ng  the  user  back  in  the  driving  seat   Observa(ons:     •  Personalized  services  are  now  common     •  But:  user  data  is  s(ll  under  control  of   separate  applica(ons   •  Result:  user  is  faced  with  mul(tude  of   distributed  personal  data,  hidden  in  tons   of  inaccessible  cookies  
  18. 18. NoTube  building  blocks  (1)   1.  TV  metadata  services   EPG  metadata  grabbers     –  from  170+  channels  (issue:  channel  URLs)   –  metadata  format:  TV  Any(me     –  real-­‐(me  service     2.  Metadata  enrichment     – Add  links  to  external  Web  vocabularies  and   repositories:  Lupedia  service  
  19. 19. NoTube  building  blocks  (2)   3.  Linked  Open  Data  for  TV   –  Access  services  to  major  vocabularies     –  Alignment  services  between  major  vocabularies,  where   needed  (e.g.  genre  typologies)   4.  User  acKvity  streams   –  Standard  for  ac(vity  stream  representa(on,  i.e.  Atom   Ac(vity  Stream   –  Access  services  to  ac(vity  streams,  e.g.  YouTube,  TwiVer,  ..   –  Trusted  access  to  “friend”  informa(on,  e.g.   implementa(on  of  standard  like  OAuth  2.0  
  20. 20. NoTube  building  blocks  (3)   5.  User  profiling   Services  for  genera(ng  user  preferences    –  “Beancounter”    –  abstrac(ons  from  ac(vity  stream     User-­‐model  representa(on  based  on  FOAF,  i.e.  weighted   interests  and  considering  context   6.  Recommender  services     Collabora(ve  recommenders,  e.g.  preferences  of  friends   Content-­‐based  recommenders,  e.g.  program  about  Alma  Mahler     program  about  Walter  Gropius     Experiment  with  mix  of  these  recommenders  for  single  users   and  small  groups  of  users,  e.g.  families,  friends  
  21. 21. Usage  scenario  &  demo  
  22. 22. http://notube.tv Thank  you   Ques(ons?   A world of opportunities is opening!

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