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Linked Data Applications - WWW2010


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These are the Linked Data Applications slides that we presented at the Consuming Linked Data tutorial at WWW2010 in Raleigh, NC on April 26, 2010.

This slide set was not part of our tutorial that was presented at ISWC2009

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Linked Data Applications - WWW2010

  1. 1. Linked  Data  Applica/ons   Consuming  Linked  Data  Tutorial   World  Wide  Web  Conference  2010  
  2. 2. What  is  a  Linked  Data  applica/on   •  So@ware  system  that  makes  use  of  data  on  the   web  from  mul/ple  datasets  and  that  benefits   from  links  between  the  datasets  
  3. 3. Characteris/cs  of  Linked  Data   Applica/ons   •  Consume  data  that  is  published  on  the  web  following  the   Linked  Data  principles:  an  applica/on  should  be  able  to   request,  retrieve  and  process  the  accessed  data   •  Discover  further  informa/on  by  following  the  links   between  different  data  sources:  the  fourth  principle   enables  this.   •  Combine  the  consumed  linked  data  with  data  from   sources  (not  necessarily  Linked  Data)     •  Expose  the  combined  data  back  to  the  web  following  the   Linked  Data  principles     •  Offer  value  to  end-­‐users      
  4. 4. Researchers  Map  
  5. 5. Music  Bore   hSp://    
  6. 6. Seman/c  Search  
  7. 7. Linked  Data  and  E-­‐Learning   •  Netex  –   •  Enrich  their  e-­‐learning  content  with  Dbpedia   and  Flickrwrapper  
  8. 8. 1st  Linked  Data-­‐a-­‐thon   •  Co-­‐located  at  ISWC2009   •  Spontaneous  and  organized  in  a  few  days   •  Three  day  hacking  session   •  Goal  was  to  develop  an  innova/ve  applica/on   that  showcase  the  virtues  of  Linked  Data.   •  8  par/cipa/ng  groups  
  9. 9. Winners   •  United  States  Linked  Data  Overlay   –  Use  Linked  Data  about  geographical  loca/ons  and   display  it  on  Google  Earth.   •  www.diversity-­‐   –  Web  and  Image  search  engine  augmented  with  Linked   Data   –  Pictures  of  David  Beckham  playing  football  in  the   different  clubs  he  has  played  for   •  Find  tradi/onal  Chinese  medicine  as  an   alterna/ve  to  western  drugs   •  iGoogr:  Imagine  Google  was  using  Good  Rela/ons   vocabulary  for  e-­‐commerce  
  10. 10. 1st  Linked  Data-­‐a-­‐thon  was  a  huge   success  and  we  learned  a  lot  
  11. 11. We  asked  ourselves…   •  What  tools  were  used?   •  What  datasets  were  used?   •  How  was  auto  discovery  achieved?   •  How  were  the  queries  wriSen?   •  Which  vocabularies/ontologies  were  used?   •  How  was  the  performance  of  the  applica/on?   •  How  trustworthy  was  the  data?  
  12. 12. Lessons  Learned   •  Par/al  Unreliability  of  Infrastructure   –  Querying  on-­‐the-­‐fly   –  Overhead  of  transla/ng  HTTP  URIs  to  SPARQL,   then  to  SQL  and  then  back   •  Lack  of  Interlinking   •  Cross  dataset  querying  is  a  challenge   •  Ignorance  to  licensing  and  informa/on  quality   •  Discover  relevant  Linked  Data  is  an  open   problem  
  13. 13. Ques/ons?