"EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content" presented at the Semantic Web Challenge, Open Track, of the 11th International Semantic Web Conference, Boston, USA, November 2012

1. EventMedia Live: Exploring Events
Connections in Real-Time to
Enhance Content
Houda Khrouf, Vuk Milicic and Raphaël Troncy
{khrouf,milicic,troncy}@eurecom.fr
@rtroncy
Semantic Web Challenge, ISWC 2012, Boston

2. Events through the web …
Event Directories
Allows opportunistic/serendipitous discovery
Limited social features and Information
incompleteness (low coverage, weak decision)
Media Platforms
Aids remembering and sharing past experiences
Lack of consistent events descriptions
Social Networks Attendance: popularity and friends' whereabouts,
attendees’ opinions.
Limited discovery mechanism: need to filter out
relevant information.
Explore semantic connections to create better overviews of events
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3. Approach
 Aggregate heterogeneous data sources
 Unify into one data model using Semantic
Web technologies
 Enrich with media, social data and
background knowledge
 Identify meaningful and/or interesting
relationships between events, media, and
people
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4. User Tasks and Challenges
Goals:
 Discover PAST, PRESENT and FUTURE events
 Live, relive and predict experiences through shared media
 Enhance search and recommendations mechanisms
 Support the decision making process
Challenges
 Semantization of Data (events, media, people)
 Reconciliation of sparse and highly heterogeneous data
 User-centric design: discovering, deciding and recommending
 Scalability, Caching and Optimization
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5. Architecture
Heterogeneous data sources
Real-time Reconciliation
- Tag-based mapping
Data Crawling
RDF Conversion
- Instance matching
RSS Update
Web Applications
SPARQL
REST API (Elda)
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6. Data Crawling
 Various API specifications
 Policy management
 Requests chaining
 Different input and output schemas
 Time-consuming
A Restful Service leveraging the commonalities of API specifications
A Web dashboard to easily handle data collection and interlinking
http://eventmedia.eurecom.fr/dashboard/
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7. Data Modeling – LODE ontology
LODE is a minimal model that encapsulates the factual properties of
events: What, Where, When and Who.
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8. Real-Time Reconciliation
Precise time
agents
Cost & description
Media
Participants
Need for semantic connections between events, media, agents and locations
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9. Events and Media connections
APIs
Machine tags
1.7 million images over
“lastfm:event=”
126.000 events
“upcoming:event=”
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10. Live Update
Per week
• 1500 Photos
Tag = *:event • 130 events
Number of Items posted during the last days Number of Items posted per days of the week
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11. Instance Matching
 Events/Agents/Locations semantic connections
 SPARQL-based Instance matching
 Filtering by the issuance date
 Challenges
 Structural heterogeneity
 Different representations of the same value
 User-generated knowledge featuring human mistakes
 Different granularities between data sources
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12. Events connections
 The events similarity is a mutual agreement of their factual properties
 Based on top-k dependencies between properties
p1 p2 dependency
1st level
Minimal conditions to fetch
title1 title2 0.30
similar events using SPARQL place1 place2 0.28
title1 agent2 0.26
2nd level
Refine the results agent1 agent2 0.21
description1 title2 0.16
 Evaluation: Precision: 96% and Recall: 94%
 Ground truth of 300 matched instances
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13. Agents & Locations connections
 Agents connections (precision-oriented)
 Internal reconciliation + DBpedia, Musicbrainz and BBC
 Label matching + contextual filtering (Cosine)
 Granularity? (1 to N mappings)  Cosine inequality
Cosine (d, a+b) > Cosine (d ,a)
 Locations connections (precision-oriented)
 Internal reconciliation + DBpedia and Foursquare
 It is straightforward thanks to the consistency of locations descriptions
 Top-k Google search
 Geographical distance
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14. About the dataset
Event Agent Location Photos User
Last.fm 61,173 62,577 15,917 1;464,885 130,249
Upcoming 19, 276 15 10,269 308,148 3,932
Eventful 45,204 8,725 18,763 3,028 321
Total 125,650 71,313 44,948 1,760,787 159,263
DBpedia MusicBrainz Eventful
Last.Fm 13064 34247 2865
Eventful 2515 3616 -
 SPARQL endpoint: http://eventmedia.eurecom.fr/sparql
 Restful API : http://eventmedia.eurecom.fr/rest/event/
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15. Web Application
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16. Web Application
 Challenges:
 Modern single-page app on top of semantic technologies
 Fast, user-friendly and responsive UI
 Dealing with high variability of data
 Technologies:
 Backbone.js: MVC framework enables complex UI,
provides flexibility
 Elda Linked Data API maps SPARQL queries to RESTful
URLs
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17. What's the added value of SW?
 Data model (RDF) + URIs for all objects +
decentralized semantics (re-use of ontologies) =
life MUCH easier for data integration
 Re-use data of others …
 DBpedia, MusicBrainz, BBC, Foursquare, etc.
 … and expose our reconciled data (CKAN)
 Toolkits:
 Virtuoso: triple store + SPARQL endpoint + RDF publishing
 Silk / Knofuss: several similarity metrics for instance matching
 Elda (Linked Data API)
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18. Semantic Web Challenge criteria
 Attractive/Functional UI: fluid navigation, multimedia
 Scalable application: 40 million triples and counting
 Novelty: real-time events connections discovery
 Use of dynamic data: social media, rss feeds
 Cross languages, cross devices: phones, tablets
 Commercial potential: ask the entrepreneurs here!
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19. What's on this evening?
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20. What's on this evening?
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21. Demo: Live your event
http://eventmedia.eurecom.fr
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22. Future Work
 User interface: aggregated views of events, agents,
locations and users.
 Opinionated content retrieval: social networks, online
newspapers, etc.
 Identify Interests and provide recommendations
 Analysis of social networks activities around real-world
events (behavioral patterns)
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23. http://www.slideshare.net/troncy
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