There exists a strong interdependencies among dynamics and social interactions on the online world and the ones taking place in the real world but still, until recently, there has been a lack of real data spanning across online and offline realities. The Live Social Semantics application that I will present, overcomes this gap. It integrates data about people from (a) their online social networks and tagging activities, (b) their publications and co-authorship networks from semantic repositories, (c) their real-world face-to-face contacts collected via a network of wearable active sensors. The two papers that I will present, explain the architecture of the Live Social Semantic application, investigate the data collected by it during its deployment at three major conferences. In particular the analysis stresses the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns.

1. Semantics, sensors, and the social web: The live social semantics experiments MyriamLeggieri DERI, NUI Galway firstname.lastname@deri.org Wednesday, 25thMay 2011 DERI, Reading Group 1

2. Semantics, sensors and the social web: Paper Details Title: “Semantics, sensors, and the social web: The live social semantics experiments” Authors Martin Szomszor- City eHealth Research Centre, UK CiroCattuto- ISI Foundation, Turin, Italy Wouter Van den Broeck - ISI Foundation, Turin, Italy Alain Barrat - Centre de Physique Théorique, Marseille, France HarithAlani - Knowledge Media Institute, The Open University, UK Year 2010 Conferences 7th Extended Semantic Web Conference (ESWC2010) 2

3. Semantics, sensors and the social web: Overview Motivation State of The Art Live Social Semantics (LSS) Stack Architecture Contact Tracking RDF for Contact Data RDF for Tagging Data Integration of Personal Data Connection modalities TAGora Sense Repository Profile Building Visualization Deployment results 3

4. Semantics, sensors and the social web: Motivation Networking: crucial component of conference activities Conference organizers are keen to enhance the social experience  Matchmaking Services Enhanced by Interests that trascend scientific domain 4

7. Detect connection among people based on

8. Online social networks

9. Topics of interest

10. Realtime updated real-world social networkhttp://www.sciencegallery.com/infectious

11. Semantics, sensors and the social web: Live Social Semantics - stack Live Social Semantics Web2.0 Linked Data Real World Delicious semanticweb.org rkbexplorer.com acm, dblp, citeseer … 7

12. Semantics, sensors and the social web: Live Social Semantics - Architecture 8 4store Aggregator Local Server Social Semantics Real World RDF Cache Real World Contact Data RFID Badges

13. Semantics, sensors and the social web: LSS Acrhitecture - Contact Tracking Local Server Multi-channel bi-directional radio communication Exchange of low-power signals shielded by the human body Contacts recorded only if “in-front-of” position detected UDP packets from RFID readers To a central server Forwarded to a post-processing server Instantaneous contact graph Cumulative proximity relation weighted graph 9

14. Semantics, sensors and the social web: LSS Architecture – RDF for Contact Data http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1410 http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1515 hasPhysicalContact contactWith http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/contact/day3/1410/1515 contactDate "2009-06-03"^^<http://www.w3.org/2001/XMLSchema#date> contactDuration "00:01:43"^^<http://www.w3.org/2001/XMLSchema#time> 10 10

15. Semantics, sensors and the social web: LSS Architecture 11 COP + Publications Profile Builder dbtune.org RKBExplorer.com Publications dbpedia.org data.semanticweb.org Consumes Tagging Data TAGora Sense Repository Extractor Daemon Delicious Social Tagging Social Networks Web Based Systems Flickr mbid - > dbpediauri tag -> dbpediauri Lastfm Returns Profile of Interests Contacts Facebook Connect API 4store RFID Readers Aggregator Local Server Social Semantics Real World RDF Cache Real World Contact Data RFID Badges

16. Semantics, sensors and the social web: LSS Architecture – Profile Builder 1212 Web2.0 Linked Data LastFM artists semanticweb.org rkbexplorer.com Delicious acm, dblp, citeseer … DBtune TAGora tagging ontology (Extractor Daemon) Tag Dpedia TAGora Sense Repository

17. Semantics, sensors and the social web: LSS - Integration of Personal Data (1/2) http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/foaf/1 Martin Szomszor Delicious Tagging and Network RFID Contact Data http://tagora.ecs.soton.ac.uk/delicious/martinszomszor http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1410 Flickr Tagging and Contacts Conference Publication Data http://tagora.ecs.soton.ac.uk/flickr/7214044@N08@N08 http://data.semanticweb.org/person/martin-szomszor/ Lastfm favourite artists and friends Past Publications, Projects, Communities of Practice http://tagora.ecs.soton.ac.uk/lastfm/count-bassy http://southampton.rkbexplorer.com/id/person-05877 Facebook contacts http://tagora.ecs.soton.ac.uk/facebook/613077109 13

18. Contact data, FB friends, Delicious tags etc each stored in distinct graphs Advanatges: Approximates a distributed Linked Data scenario Different processes can update the data model asynchronously Push/Pull whenever from wherever to the visualization client 14 Semantics, sensors and the social web: LSS - Integration of Personal Data (2/2) 14

19. Semantics, sensors and the social web: LSS – connection modalities 15 Delicious Folksonomies, The Semantic Web, and Movie Recommendation CiroCattuto Martin Szomszor Live Social Semantics Publications www.tagora-project.eu Projects 15

20. Semantics, sensors and the social web: LSS - RDF for Tagging Data isFilteredTo didYouMean GlobalTag hasGlobalFrequency xsd:integer Tag rdfs:label DomainTag xsd:string hasDomainFrequency xsd:integer hasGlobalTag UserTag hasNextSegment (f) hasUserFrequency xsd:integer hasDomainTag TagSegment usesTag segmentTag (f) tagAssigned FinalTagSegment hasTagSequence (f) hasPost Tagger Post http://tagora.ecs.soton.ac.uk/schemas/tagging# taggedResource xsd:dateTime http://www.w3.org/2001/XMLSchema# subclass property taggedOn Resource (f) = functional property 16

21. Semantics, sensors and the social web: LSS - TAGora Sense Repository (1/4) Tag filtering service + metadata about tags and their possible senses (SPARQL endpoint, REST API) Resource Index creation XML dump of all Wikipedia pages  title, redirection links, disambiguation links, keywords and their frequencies For each page it stores list of all incoming links + total links Link to Dbpedia by owl:sameAs 17 17

22. Semantics, sensors and the social web: LSS - TAGora Sense repository (2/4) Search for senses (of DBpedia resources) Search against resource titles + redirection and/or disambiguation links Weight of sense “r” for tag “T”: #incomingLinksR / #incomingLinkAllSensesForT Senses associated with general concepts receive higher weight Selected sense = Global Tag in TSR associated with the User Tag in LSS More than 1 sense exists  Tag Disambiguation 18 18

23. Semantics, sensors and the social web: LSS – TAGora Sense Repository (3/4) TAGora Sense Repository tagging:hasGlobalTag tagging:GlobalTag tagging:UserTag http://tagora.ecs.soton.ac.uk/tag/ontologymapping http://tagora.ecs.soton.ac.uk/delicious/tag/ontologymapping disam:hasPossibleSense http://dbpedia.org/resource/Semantic_Integration tagging:UsesTag tagging:Tagger foaf:Person http://tagora.ecs.soton.ac.uk/delicious/martinszomszor foaf:interest foaf:Person http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/foaf/1 owl:SameAs 19

24. Semantics, sensors and the social web: LSS - TAGora Sense Repository (4/4) Tag disambiguation Term Vector = Context = other tags used to annotate the same resource by the same user Term frequency vector = frequencies of keywords in the given sense (Dbpedia resource) Cosine similarity  candidate resource list of interest C 20 apple, film, 1980, .. apple, inc, computer, .. apple, iphone, computer, .. apple, tree, fruit, .. 20

25. Semantics, sensors and the social web: LSS - Profile Building (1/2) 1) Disambiguate Tags cosine similarity between user co-occurrence vector and term frequency vector from concept Choose Sense if above threshold (0.3) or single sense 2) Calculate Interest Weights weight w = fr ∗ ur ; fr = total frequency of all tags disambiguated to sense r ur = time decay factor = ⌈days(r)/90⌉ 3) Create Interest List If more than 50 interests are suggested, rank by weight and suggest the top 50 Users must verify the list before it is published 21

26. Semantics, sensors and the social web: LSS – Profile Building (2/2) Interest list publishing must be approved by users 22

27. Semantics, sensors and the social web: LSS - Visualization (1/2) Spatial View Accessible from publicly exposed main monitor Participans: yellow disc / FB picture Contacts: yellow edges Weight of contacts: edge thickness and opacity Type of contacts: edges decorated by online sources icons RFID readers: labelled grey shapes Coarse-grained localization of participants 23 23

28. Semantics, sensors and the social web: LSS – Visualization (2/2) User-focus view Accessible from any web-browser W = Ongoing + cumulative contacts for a given user Close relevant triangles: contacts linked to both the given user and any other one Subsection of neighbourhood that is relevant for user networking at the moment 24 24

29. Semantics, sensors and the social web: Deployments European Semantic Web Conference (ESWC2009) Attendees 305 187 Participated in LSS 139 of them registered online Hypertext (2009) Attendees 150 113 Participated in LSS 97 of them registered online Totals 455 Attendees 300 Participated in LSS 236 registered online 21% people took a badge but did not register 25

30. Semantics, sensors and the social web: Deployment Results Declaration of SNS Accounts 26

31. Profiles of Interest Semantics, sensors and the social web: Deployment Results 27

32. Accuracy of DBPedia Senses Semantics, sensors and the social web: Deployment Results 28

33. Survey Results Semantics, sensors and the social web: Deployment Results Why some users did register on the LSS site but did not enter any social networking account: 29

34. Semantics, sensors and the social web: Future Work Allow individuals to link to their own foaf profiles More SNS sites: i.e. Twitter, LinkedIn Document and Advertise Linked Data Interface Support other applications in exploiting the data Recommend Contacts What features are most predictive of face-to-face contact Align Tagging Ontology with SIOC 30

35. Semantics, sensors and the social web: Conclusions What tags correspond to interests? Locations and topics are useful, but other terms are not TF / IDF Approach It’s not that useful to find out we are all interested in RDF and the Semantic Web Making use of the Category hierarchy If I’m interested in Facebook, Flickr, Last.fm, Delicious, etc, I can extrapolate the interest Online_Social_Networks 31

36. Social dynamics in conferences: Paper Details Title: “Social dynamics in conferences: analyses of data from the Live Social Semantics application” Authors Martin Szomszor- City eHealth Research Centre, UK CiroCattuto- ISI Foundation, Turin, Italy Wouter Van den Broeck - ISI Foundation, Turin, Italy Alain Barrat - Centre de Physique Théorique, Marseille, France HarithAlani - Knowledge Media Institute, The Open University, UK Year 2010 Conferences International Semantic Web Conference (ISWC2010) 32

37. Social dynamics in conferences: Overview Motivation Analysis description Analysis results F2F interaction Frequent users Senior users Online vs offline popularity Netwoking with online and offline friends Discussion and Future Work Personal Remarks 33

38. Social dynamics in conferences: Motivation Correlation among features of those users which are connected in a social network Long-standing problem in social science, ecology and epidemiology “Assortative Mixing” pattern: tendency of network nodes to link with others having similar properties LSS deployments results are analyzed Purpose: novel insights into the comparability of online and offline networks Better understand impact of specific parameters on the social contact behaviour of individuals and groups 34 34

39. Social dynamics in conferences: Analysis description (1/2) Face-to-face interactions in scientific conferences Contacts frequency and duration compared across the 3 deployments Networking behaviour of frequent users Consider only users who participated in 2 deployments quantitatively and qualitatively, compared with one-time users Scientific seniority of users Correlation among seniority of users and seniority of their F2F contacts General strenght of seniority user’s social network Correlation among seniority of users and # of their Twitter followers 35 35

40. Social dynamics in conferences: Analysis description (2/2) Comparison of F2F contact network with Twitter and Facebook Are people with strong online social presence very active even in F2F networking? And vice versa Social networking with online and offline friends Contact networks analyzed considering co-authorship and online social networking relationships 36 36

41. Social dynamics in conferences: Analysis Result 37 37

42. Social dynamics in conferences: Analysis Result Returning attendees have larger average interaction time and frequency, especially among each other 38 38

43. Social dynamics in conferences: Analysis Result 39 During different conferences People interacted with different contacts Time spent in these interaction is very similar 39

45. # papers authored at semantic web related conferences

46. H-index from scholarometer.indiana.edu40

47. Social dynamics in conferences: Analysis Result Seniority and Social Activity Interact with more distinct people Spend more time in F2F interactions Higher amount of interactions 41 41

49. Third peak: developer42

50. Social dynamics in conferences: Analysis Result 43 People most active in F2F contacts do not necessarily have the largest online social network 43

51. Social dynamics in conferences: Analysis Result People sharing an online or professional link meet more often The average number of encounters and the total time spent in interaction is higher for co-authors 44 44

52. Social dynamics in conferences: Conclusions and Future Work Behaviour in F2F networking is very similar across events Limitation: only people who used LSS were considered Future work: RFID tags with on-board memory to enable F2F contacts to be logged regardless of distance from RFID readers Future work: Other possible parameters i.e. age, affiliations, chronology of social relationships etc. Future work: Consider account date of creation + whether user is active over there 45 45

53. Personal Remarks User profile data integration usefully enhanced by real-life ongoing interactions Not yet taken advantage from semantic representation neither of tags nor of contacts Tag hierarchy from Dbpedia concepts to find more specific topics to refine contact recommendation based on similar topics SPARQL endpoint on contact data Dbpedia descriptions demonstrated to not being enoughly accurate Not enough justification of contact recommendations by i.e. listing common topics of interest Absence of a Privacy Manager at a triple level 46 46