Linked  Data  Applica/ons  

Consuming  Linked  Data  Tutorial  World  Wide  Web  Conference  2010  

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  

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      

Researchers  Map  

Music  Bore  

hSp://vimeo.com/5561292    

Seman/c  Search  

Linked  Data  and  E-­‐Learning  

•  Netex  –  www.netex.es  •  Enrich  their  e-­‐learning  content  with  Dbpedia  and  Flickrwrapper  

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  

Winners  

•  United  States  Linked  Data  Overlay  – Use  Linked  Data  about  geographical  loca/ons  and  display  it  on  Google  Earth.  

•  www.diversity-­‐search.info  – 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  

1st  Linked  Data-­‐a-­‐thon  was  a  huge  success  and  we  learned  a  lot  

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?  

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  

Ques/ons?