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.

1. 9/13/13
1
The story behind
Miel Vander Sande
@Miel_vds
Multimedia narration of automatically discovered paths in Linked Data

3. VIDEO CODING & COMPRESSION
SEMANTIC & SOCIAL WEB
Me
MEDIA Annotation & ANALYSIS
GAMING TECHNOLOGY

4. LINKED DATA?

5. SEMANTIC WEB?
LINKED DATA?

6. We put a layer on the
existing web Adding
machine understandable
descriptions TO the
underlying data USING
graph structures

8. 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

10. Computers.
Internet.

11. Linked Data tangible?
HOW DO WE Make

12. AGENDA1 CONCEPT
3 PATH NARRATION
2 PATHFINDING
4 DEMO

13. AGENDA1 CONCEPT
3 PATH NARRATION
2 PATHFINDING
4 DEMO

14. ÜBERDEMODEMONSTrate Linked DaTA & Semantic web.
Represent our lab.
Fast.
Sexy.
Modern.

20. 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

21. AGENDA1 CONCEPT
3 PATH NARRATION
2 PATHFINDING
4 DEMO

22. 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!

23. Laurens De Vocht
laurens.devocht@ugent.be
@laurens_d_v
LEADing & Ongoing research

24. ?
Paris Barack
Obama
Bertrand
Delanoë
Catholic
Church
Joe Biden
mayor religion religion of vicepresident of
Result
QUERY
Paris
Barack
Obama

25. AAdjacency MATRIX
WEIGHTED EDGES
HEURISTIC
*
ITERATIVE ALGORITHM

26. AAdjacency MATRIX
WEIGHTED EDGES
HEURISTIC
*
ITERATIVE ALGORITHM

27. 0
PARIS
1
BARACK
OBAMA
Resources
:Paris
0
:Barack_Obama
1

28. 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

29. AAdjacency MATRIX
WEIGHTED EDGES
HEURISTIC
*
ITERATIVE ALGORITHM

30. 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

31. 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))

32. AAdjacency MATRIX
WEIGHTED EDGES
HEURISTIC
*
ITERATIVE ALGORITHM

33. Jaccard distance
:monument
:language
:capital
:mayor
:Paris
:France
unSHARED PREDICATES BETWEEN RESOURCES
FAVOR CLOSELY RELATED RESOURCES

34. 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

35. 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

36. 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

37. OPTIMIZATION:
Node centrality based rank reduction
SEACH FOR HUBS: NODES WITH Many Links
PageRank

38. Hitrate above 90%
Testset of 10 000 paths
among 200 popular cities, artists and countries in
DBPedia (10M entities)

39. Average OF 4 steps

40. Exponential space complexity

41. Linear time complexity

42. Over 60% of paths found in less than 2000ms

43. AGENDA1 CONCEPT
3 PATH NARRATION
2 PATHFINDING
4 DEMO

44. 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

45. Slide
Presenter
TopicToTopic
Topic
Topic
Youtube
GoogleMaps
GoogleImage
…
…
Outroduction
Text-to-
speech
Pathfinding
Title
Slide
Composite
User
HTML5 & JS WEB Application
= Slidegenerator
Introduction

46. AGENDA1 CONCEPT
3 PATH NARRATION
2 PATHFINDING
4 DEMO

47. 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

48. 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/