Live Social Semantics @ ISWC2009


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Paper presented at the International Semantic Web Conference (ISWC) 2009

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Live Social Semantics @ ISWC2009

  1. 1. Live Social Semantics<br />A novel application that integrates data from the semantic web, online social networks, and a real-world face-to-face contact sensing platform.<br />Martin Szomszor<br />University of Southampton<br />
  2. 2. Outline<br />History<br />Where Live Social Semantics came from<br />LSS Architecture<br />Tracking Face-to-Face Contacts<br />Integrating and Managing Data<br />Building Profiles of Interest<br />Video Demonstration<br />LSS at ESWC2009<br />Future Work<br />
  3. 3. Live Social Semantics<br />History<br />Dagstuhl Seminar on Social Web Communities (Sept 2008)<br />
  4. 4.<br />This projects aims to shed light on patterns in social dynamics and coordinated human activity. We do so by developing and deploying an experimental social interaction sensing platform. This platform consists of portable sensing device and software tools for aggregating, analyzing and visualizing the resulting data.<br /><br />
  5. 5. Southampton<br /><ul><li>Automatic Generation of Profiles of Interest using Cross-Folksonomy Data</li></ul>[2] Szomszor, M., Alani, H., Cantador, I., O'Hara, K. and Shadbolt, N. (2008) Semantic Modelling of User Interests based on Cross-Folksonomy Analysis. In: 7th International Semantic Web Conference (ISWC), October 26th - 30th, Karlsruhe, Germany.<br />
  6. 6. ISI (Turin) Meeting March 2009<br />
  7. 7. LSS – Proposed Features<br />Contact Histories<br />“Hey, I remember talking to this person, but I don’t know their name / email / institution”<br />People you might know <br />“Who are the people in my social networks / community of practice who are also attending the conference? What papers are they presenting”<br />Profiles of Interest<br />“I’d like to expose the things that I’m interested in to other participants, including extra-academic data”<br />
  8. 8. Features NOT Required<br />We are not concerned with tracking an individual’s exact location. The focus of LSS is to log social interactions (face-to-face contact)<br />We don’t want to track people outside the conference area<br />Participation<br /><ul><li>Participation is voluntary
  9. 9. Association of your RFID badge to your real identity is voluntary
  10. 10. You can participate using only an anonymous id</li></li></ul><li>LSS Stack<br />Live Social Semantics<br />Web2.0<br />Linked Data<br />Real World<br />Delicious<br /><br /><br />acm, dblp, citeseer …<br />
  11. 11. Active RFID Contact Tracking<br />Local Server<br />
  12. 12. ESWC2009 Map<br />
  13. 13. Active RFID Proximity Detection<br />spatial resolution ~ 1 meter<br />anisotropy - face-to-face<br />temporal resolution ~ 5-20 seconds<br />unobtrusive<br />scalable<br />low cost (~15 Euro per badge – reusable)<br />easily deployable<br />distributed<br />
  14. 14. RDF Representation of Contact Data<br /><br /><br />hasPhysicalContact<br />contactWith<br /><br />"2009-06-03"^^<><br />contactDate<br />"00:01:43"^^<> <br />contactDuration<br />
  15. 15. Architecture<br />COP + Publications<br /><br />Profile<br />Builder<br /><br />Publications<br /><br /><br />Consumes<br />Tagging Data<br />TAGora Sense<br />Repository<br />Extractor<br />Daemon<br />Delicious<br />Social Tagging<br />Social Networks<br />Web Based Systems<br />Flickr<br />mbid - > dbpediauri<br />tag -> dbpediauri<br />Lastfm<br />Returns Profile<br />of Interests<br />Contacts<br />Facebook<br />Connect API<br />4store<br />RFID Readers<br />Local Server<br />Social Semantics<br />RDF Cache<br />Aggregator<br />Real World<br />Real World Contact Data<br />RFID Badges<br />
  16. 16. How are you connected?<br />Delicious<br />Folksonomies, The Semantic Web, and Movie Recommendation<br />CiroCattuto<br />Martin<br />Szomszor<br />Live Social Semantics<br />Publications<br /><br />Projects<br />
  17. 17. Distinct, Separated Identity Management<br /><br />Martin<br />Szomszor<br />Delicious Tagging and Network<br />RFID Contact Data<br /><br /><br />Flickr Tagging and Contacts<br />Conference Publication Data<br /><br /><br />Lastfm favourite artists and friends<br />Past Publications, Projects, Communities of Practice<br /><br /><br />Facebook contacts<br /><br />
  18. 18. Profiles of Interest<br />tagging:hasGlobalTag<br />TAGora Sense Repository<br />tagging:UserTag<br />tagging:GlobalTag<br /><br /><br />disam:hasPossibleSense<br />tagging:UsesTag<br /><br />tagging:Tagger<br />foaf:Person<br /><br />foaf:interest<br />foaf:Person<br /><br />owl:SameAs<br />
  19. 19. Profile Building<br />1) Disambiguate Tags<br />cosine similarity between user co-occurrence vector and term frequency vector from concept<br />Choose Sense if above threshold (0.3) or single sense<br />2) Calculate Interest Weights<br />weight w = fr ∗ ur , where fr is the total frequency of all tags disambiguated to sense r, and ur is a a time decay factor. The factor ur = ⌈days(r)/90⌉<br />3) Create Interest List<br />If more than 50 interests are suggested, we rank by weight and suggest the top 50<br />Users must verify the list before it is published<br />
  20. 20. Live Social Semantics Video<br /><br />
  21. 21. LSS @ ESWC2009<br /><ul><li>4 Days (1-4 June 2009)
  22. 22. >300 Attendees, 187 of which participated in the experiment
  23. 23. Each participant was issued with a uniquely number RFID badge
  24. 24. Users could register their badge number on a website, and associate it to their name, institution, email, and social networking accounts
  25. 25. Out of the 187 who collected a badge, 139 registered their account on the website</li></li></ul><li>
  26. 26. SNS Usage Statistics<br />
  27. 27. Survey Results<br />After the conference, we emailed the users who did register on our site, but did not enter any social networking accounts. The aim was to understand the reasons why:<br />
  28. 28. Future Work<br />Allow individuals to link to their own foaf profiles<br />More SNS sites:<br />Twitter, LinkedIn, etc…<br />Document and Advertise Linked Data Interface<br />Support other applications in exploiting the data<br />Recommend Contacts<br />What features are most predictive of face-to-face contact<br />
  29. 29. Building Better Profiles<br />What tags correspond to interests?<br />Locations and topics are useful, but other terms are not<br />TF / IDF Approach<br />It’s not that useful to find out we are all interested in RDF and the Semantic Web<br />Making use of the Category hierarchy<br />If I’m interested in Facebook, Flickr,, Delicious, etc, I can extrapolate the interest Online_Social_Networks<br />
  30. 30. University of Southampton<br />Acknowledgements<br />CiroCattuto, Wouter Van den Broeck, Alain Barrat<br />HarithAlani, Martin Szomszor, GianlucaCorrendo<br />
  31. 31. Thanks for your attention<br />
  32. 32. Presence of Attendees HT2009<br />
  33. 33. Number of cliques HT2009<br />