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The Story behind Everything Is Connected: Multimedia narration of automatically discovered paths in Linked Data

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Everything is Connected (http://everythingisconnected.be) is a Linked Data application for automatically generating a story between two concepts in the Web of Data, based on formally described links. A path between two concepts is obtained by browsing linked open datasets; the path is then enriched with multimedia presentation material for each node in order to obtain a full multimedia presentation of the found path. An efficient technique combining pre-processing and indexing of RDF datasets is used, which is able to find paths in a couple of seconds.

Published in: Technology, News & Politics

The Story behind Everything Is Connected: Multimedia narration of automatically discovered paths in Linked Data

  1. 1. 9/13/13   1   The story behind Miel Vander Sande @Miel_vds Multimedia narration of automatically discovered paths in Linked Data
  2. 2. VIDEO CODING & COMPRESSION   SEMANTIC & SOCIAL WEB   Me MEDIA Annotation & ANALYSIS GAMING TECHNOLOGY  
  3. 3. LINKED DATA?
  4. 4. SEMANTIC WEB? LINKED DATA?
  5. 5. We put a layer on the existing web Adding machine understandable descriptions TO the underlying data USING graph structures
  6. 6. It’s Like the WEB but instead of Linking Pages we Link data and enrich it semantically so Generic intelligent agents can UNDERSTAND and BROWSE DATA autonomously
  7. 7. Computers. Internet.
  8. 8. Linked Data tangible? HOW DO WE Make  
  9. 9. AGENDA1 CONCEPT 3 PATH NARRATION 2 PATHFINDING 4 DEMO
  10. 10. AGENDA1 CONCEPT 3 PATH NARRATION 2 PATHFINDING 4 DEMO
  11. 11. ÜBERDEMODEMONSTrate Linked DaTA & Semantic web. Represent our lab. Fast. Sexy. Modern.
  12. 12. Client Server SOURCE DBPedia Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 … Topic N Playback SOLR Pathfindin g RESTAPI SIREn Resource Description Lookup DEST INAT IO N
  13. 13. AGENDA1 CONCEPT 3 PATH NARRATION 2 PATHFINDING 4 DEMO
  14. 14. Client Server SOURCE DBPedia Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 … Topic N Playback SOLR Pathfindin g RESTAPI SIREn Resource Description Lookup DEST INAT IO N PATHFINDING SERVICEREASONABLE COMPLEXITY HIGH PERFORMANCE!
  15. 15. Laurens De Vocht laurens.devocht@ugent.be @laurens_d_v LEADing & Ongoing research
  16. 16. ? Paris Barack Obama Bertrand Delanoë Catholic Church Joe Biden mayor religion religion of vicepresident of Result QUERY Paris Barack Obama
  17. 17. AAdjacency MATRIX WEIGHTED EDGES HEURISTIC * ITERATIVE ALGORITHM
  18. 18. AAdjacency MATRIX WEIGHTED EDGES HEURISTIC * ITERATIVE ALGORITHM
  19. 19. 0 PARIS 1 BARACK OBAMA Resources :Paris 0 :Barack_Obama 1
  20. 20. 0 2 3 PARIS Eiffel tower FRANCE . . . 1 4 5 BARACK OBAMA JOE BIDEN UNITED STATES . . . Resources :Paris 0 :Barack_Obama 1 :Eiffel_Tower 2 :France 3 :Joe Biden 4 :United_States 5 … 0 1 2 3 4 5 0 0 0 1 1 0 0 1 0 0 0 0 1 1 2 1 0 0 0 0 0 3 1 0 0 0 0 0 4 0 1 0 0 0 0 5 0 1 0 0 0 0
  21. 21. AAdjacency MATRIX WEIGHTED EDGES HEURISTIC * ITERATIVE ALGORITHM
  22. 22. deg(node) = sum(nodelinks) weight(parent; child) = log(deg(parent)) + log(deg(child)) A Novel Metric for Information Retrieval in Semantic Networks – Moore et al. ENCOURAGE RARE NODES
  23. 23. 0 2 3 PARIS Eiffel tower FRANCE A Novel Metric for Information Retrieval in Semantic Networks – Moore et al. deg(0) =1286 deg(2) = 2237 deg(3) = 3138 weight(0;2) = 6,46 weight(0;3) = 6,61 ENCOURAGE RARE NODES deg(node) = sum(nodelinks) weight(parent; child) = log(deg(parent)) + log(deg(child))
  24. 24. AAdjacency MATRIX WEIGHTED EDGES HEURISTIC * ITERATIVE ALGORITHM
  25. 25. Jaccard distance :monument :language :capital :mayor :Paris :France unSHARED PREDICATES BETWEEN RESOURCES FAVOR CLOSELY RELATED RESOURCES
  26. 26. 0 2 3 PARIS Eiffel tower FRANCE weight(0;2) = 6,46 weight(0;3) = 6,61 weight(2;3) = 6,7 h(0;3) = 0.75 h(0;2) = 0.65 f(x;y) = h(x;y) + weight(x;y) PRIORITY QUEue of A* h(2;3) = 0.75
  27. 27. 0 2 3 PARIS Eiffel tower FRANCE . . . 1 4 5 BARACK OBAMA JOE BIDEN UNITED STATES . . . IF NO PATH FOUND EXPAND CHILDNODES ADD TO ADJACENCY MATRIX REPEAT OR TERMINATION  
  28. 28. OPTIMIZATION: Node centrality based rank reduction   0 1 2 3 … 0 0 0 1 1 1 0 0 0 0 2 1 0 0 0 3 1 0 0 0 … Matrix INIT TIME GROWS exponentially Memory limitations! Reduce! OPTIMIZATION: Blacklisting
  29. 29. OPTIMIZATION: Node centrality based rank reduction   SEACH FOR HUBS: NODES WITH Many Links PageRank
  30. 30. Hitrate above 90% Testset of 10 000 paths among 200 popular cities, artists and countries in DBPedia (10M entities)
  31. 31. Average OF 4 steps
  32. 32. Exponential space complexity
  33. 33. Linear time complexity
  34. 34. Over 60% of paths found in less than 2000ms
  35. 35. AGENDA1 CONCEPT 3 PATH NARRATION 2 PATHFINDING 4 DEMO
  36. 36. Client Server SOURCE DBPedia Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 … Topic N Playback SOLR Pathfindin g RESTAPI SIREn Resource Description Lookup DEST INAT IO N MULTIMEDIA PRESENTATIONExtensible DYNAMIC Real-time AUTOMATIC PLAYBACK
  37. 37. Slide Presenter TopicToTopic Topic Topic Youtube GoogleMaps GoogleImage … … Outroduction Text-to- speech Pathfinding Title Slide Composite User HTML5 & JS WEB Application = Slidegenerator Introduction
  38. 38. AGENDA1 CONCEPT 3 PATH NARRATION 2 PATHFINDING 4 DEMO
  39. 39. www.Everythingisconnected.be Linked Data made tangible high performance pathfinding in Large datasets Real-time multimedia narration of Paths Miel Vander Sande – miel.vandersande@ugent.be
  40. 40. A'ribu,on   •  Photo  slide  #3:  www.sos.ca.gov   •  Photo  slide  #6:  h'p://www.flickr.com/photos/joebenjamin/   •  Photo  slide  #4:  h'p://www.flickr.com/photos/ilri/   •  Photo  slide  #7:  h'p://www.flickr.com/photos/ andresmusta/   •  Photo  slide  #11:  h'p://www.flickr.com/photos/wtlphotos/   •  Photo  slide  #12:  h'p://www.flickr.com/photos/kristylopez/   •  Photo  slide  #15:  h'p://www.so'.net/   •  Photo  slide  #21:  h'p://www.flickr.com/photos/dr/  &   h'p://www.flickr.com/photos/llimaorosa/    

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