Experiencing Events through User-Generated Media. Talk given at the 1st International Workshop on Consuming Linked Data (COLD), November 2010, Shanghai , China

1. Experiencing Events through
User-Generated Media
Raphaël Troncy <raphael.troncy@eurecom.fr>
André Fialho <andre.fialho@cwi.nl>
Lynda Hardman <lynda.hardman@cwi.nl>
Carsten Saathoff <saathoff@uni-koblenz.de>

2. What are Events?
Events are observable occurrences grouping
People Places Time
Experiences documented by Media
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3. http://www.flickr.com/photos/crsan/3697785107

4. Goals
1. Discover PAST, PRESENT and FUTURE events
2. Live, relive and predict experiences through shared media
3. Identify meaningful and/or interesting relationships
between events/media/people
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5. Searching for an event
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6. Searching for an event
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7. There’s a lot of information out there…
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8. http://www.flickr.com/photos/mwparenteau/432039783

9. Organize the mess
Event
Media
 Identify behavioral patterns (requirements)
 Make an abstraction
 Link the information
 Design the application Interface
 Let it “evolve”
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
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10. 1st Collect some opinions…
Online Survey (n=28), 2 group discussions (n=35)
Past Experiences
(Memorable Events) Existing Technologies
• Discovery • Opinions Scenarios
• Decision making • Interests Requirements
• Registering & sharing • Suggestions 1st Design Concept
• Meaningful relationships • Benefits/drawbacks
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11. 2nd Look into “real” behaviors…
 Scenario based study (2 sessions, n=15)
Opinions
Scenarios
Reenact
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12. Behavioral Patterns
 Discovery
 Invitations and recommendations
 Rely on traditional media
 Social networks (facebook - students)
 Previously attended events or venues
 Decision Making
 Who’s Joining?
 Where, When, How Much? (constraints)
 What? (e.g. type, performer, topic)
 Subjective factors (fun, atmosphere)
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13. Behavioral Patterns
 Registering and Sharing
 Communicating their experience
 Pictures and short videos (for sharing)
 Media directories and social networks
 Meaningful Relationships
 Similar categories, attributes and content
 User attendance (similar interests, behaviors)
 Repeated events (e.g. annual festivals)
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15. Behavioral Patterns
EVENT EVENT
EVENT
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16. Existing Services
 Single source with overview (?)
 Allows opportunistic/serendipitous discovery
 Limited exploration/browsing features
 Information overload (cluttered, difficult)
 Information incompleteness (coverage, decision)
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17. Ontology: Making an abstraction
What? Where? When? Who?
08/11/2010 -
http://www.flickr.com/photos/benheine/4732941129
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18. 08/11/2010 -
16/09/2009 Event-based Annotation and Exploration of Media - PetaMedia2010, Shanghai, China
Experiencing Events through User-Generated Media - COLD SYTIM, Lausanne (CH) - 18

19. Representing Events with LODE
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20. Some mappings in LODE
ABC CIDOC DUL EO LODE
atTime P4.has_time_span isObservableAt time atTime
P7.took_place_at place inSpace
inPlace hasLocation atPlace
involves P12.occurred_in_the_ hasParticipant factor involved
presence_of
hasPresence P11.had_participant involvesAgent agent involvedAgent
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21. Representing Media with Media Ontology
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23. Extracting and linking data
1.7 million images over
Machine tags
APIs 108.000 events
“lastfm:events”
Random sampling filtered by
the tag “music'' +
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24. Reasoning & Annotation
 Time, Location and Attendance
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25. Collaborative Filtering
 Disambiguate and propagate information about
attendance
 Identify Interests and provide
Recommendations
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26. How much data is there?
Event Agent Location Photos User
Last.fm 57,258 50,150 16,471 1,425,318 18,542
Upcoming 13,114 0 7,330 347,959 4,518
Eventful 37,647 6,543 14,576 0 0
Total 108,019 56,693 38,377 1,773,277 23,060
Event Title Venue City Date Photos Video
Primavera Sound 2010 Parc del Forum Barcelona 2010-05-28/30 2458 1730
Coachella Valley Music Empire Polo Indio 2008-04-25/27 2311 434
and Arts Festival Grounds
Lollapalooza Festival Grant Park Chicago 2009-08-07/09 2220 1100
SXSW Music 2008 Convention Center Austin 2008-03-12/16 2153 1890
Sziget Festival Obudai-Sziget Budapest 2008-08-12/18 2072 4620
1,248,021 geo-tagged photos by propagating information from events!
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27. Linking the Data
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28. Interlinking
 Linking Agents with
 Freebase, Dbpedia, MusicBrainz
 Linking Venues with
 Geonames, Dbpedia, Foursquare (via Uberblic)
 Linking Events with
 Last.fm, Upcoming, Eventful
 Linking Categories with
 Facebook, Eventful, Upcoming, Zevents, LinkedIn,Eventbrite,
TicketMaster
 Linking Users with
 Social Graph API
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29. The Back-end
 RDF Repository on a web server with:
 Sesame2 SPARQL endpoint
with a distributed query engine.
 A RESTful API that provides different methods and JSON
representations of resources available in the dataset.
RDF JSON
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30. Where is the data?
 RDF dump:
http://www.eurecom.fr/~troncy/ldtc2010/
(warning: you are about to download 2 GB)
 SPARQL endpoint:
http://data.linkedevents.org/sparql
 Based on Sesame2
 RESTful API
 Serialization in N3 and JSON
 Around 20 millions triples (… and counting)
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31. Translating the Ontology
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32. Interface elements
Facets Search Timeline
Events
Media
Attendance ME
Content and Background
Location (Map)
Actions Sorting
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33. Interfaces
 Perspectives
What: Event/Media Centric
Who: Social Network Visualization
When: Time centric
Where: Location Centric
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34. http://www.flickr.com/photos/cocoarmani/1315402174

35. User Interface
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36. User Interface
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37. Conclusions
 Event-based approach for users to explore,
annotate and share media
 User studies in order to:
 Find out which data are useful for which task
 Understand typical behavioral pattern
 UX can help semantics, semantics can help UX
 Outstanding challenges in:
 Interlinking and curating the data
 Scalability (efficient caching)
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38. http://www.slideshare.net/troncy
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