Sofia Angeletou's Slides from the 'What Linked Data Does, What Linked Data Needs' discussion panel.

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Sofia shares here perspective on the BBC's use of Linked Data for the Semantic Web Meet Up at King's College. …

Sofia shares here perspective on the BBC's use of Linked Data for the Semantic Web Meet Up at King's College.
http://www.meetup.com/Web-Of-Data/events/155068962/

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  • 1. Linked  data  at  the  BBC   Sofia  Angeletou     Data  Architect   Linked  Data  Pla3orm   Core  Engineering  
  • 2. overview   •  what  is  the  BBC  doing  with  Linked  Data •  what  have  we  learnt   •  what  are  our  main  challenges      
  • 3. Linked  Data  Pla3orm   •  “To  organise  the  BBC's  online  content  around   the  things  that  ma5er  to  our  audience”   •  Link,  reuse  content  and  expose  it  in  novel  and     interesAng  ways   •  Available  to  many  online  products    
  • 4. what  does  that  mean?   •  A  lot  of  data   •  Defining  the  scope  and  the  data  model  very   well  and  clearly   •  Enabling  tools  and  services,  self  provisioning  
  • 5. TRIPLESTORE   CLIENT  APIs   CLIENT  APIs   DOMAIN  APIs   LDP  CORE   LDP  WRITER   key  components   SHARED  LIBRARIES  
  • 6. data   •  creaAve  works  (content)   •  things  (reference  data)   •  ontologies   –  content   –  things   –  management  and  ownership  
  • 7. services   •  live  sites   –  /sport   –  /educaAon   –   news  pilot   •  coming  soon   –  music  news   –  radio    
  • 8. lessons  learnt   Adding  complexity    while  not  directly  adding   value  to  the  proposiAon  is  not  a  good  idea   •  “Nice  to  have”,  “cool  thing  to  do”  are  not   compelling  reasons  when  dealing  with  a   producAon,  24/7  live  pla3orm  
  • 9. lessons  learnt   •  Trying  to  be  a  good  LOD  ciAzen  should  be   done  carefully   •  Examples   –  more  than  1M  unused  FOAF  triples   –  Geonames  ontology  very  large  and  difficult  to   delete.   •  Very  hard  to  manage  unused  models  when   serving  many  clients.  
  • 10. lessons  learnt   •  CauAously  use  restricAons   –  blank  nodes   –  funcAonal  properAes   –  disjointness   –  domains  and  ranges  (cause  inference,  they  are  not   for  type  checking)  
  • 11. main  challenges     •  decommissioning  legacy   •  shared  ownership  of  common  topics   •  culture   –  tagging  with  a  purpose   –  what  is  your  proposiAon  (why  you  want  to  do  it)  
  • 12. main  principles   •  what  is  the  audience  proposiAon?   –  only  build  what  delivers  the  funcAonality  and  extend   iteraAvely   –  only  add  data  that  add  business  value   •  clear  ownership     –  content,  concepts,  ontologies   –  management  and  permissions   •  bo^om  up  approach   –  first  test,  then  generalise   –  controlled  pilot  builds  
  • 13. Thank  you   sofia.angeletou@bbc.co.uk