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Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
Semantics, Sensors and the Social Web
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Semantics, Sensors and the Social Web

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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 …

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.

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  • Instantaneous contact graph = time-dependent adjacency matrix
  • Dbtune maps with MusicBrainz IDs
  • Queries must be expanded and run over multiple SPARQL endpoints
  • Transcript

    1. Semantics, sensors, and the social web: The live social semantics experiments<br />MyriamLeggieri<br />DERI, NUI Galway<br />firstname.lastname@deri.org<br />Wednesday, 25thMay 2011<br />DERI, Reading Group<br />1<br />
    2. Semantics, sensors and the social web: Paper Details<br />Title: “Semantics, sensors, and the social web: The live social semantics experiments”<br />Authors<br />Martin Szomszor- City eHealth Research Centre, UK<br />CiroCattuto- ISI Foundation, Turin, Italy<br />Wouter Van den Broeck - ISI Foundation, Turin, Italy<br />Alain Barrat - Centre de Physique Théorique, Marseille, France<br />HarithAlani - Knowledge Media Institute, The Open University, UK<br />Year<br />2010<br />Conferences<br />7th Extended Semantic Web Conference (ESWC2010)<br />2<br />
    3. Semantics, sensors and the social web: Overview<br />Motivation<br />State of The Art<br />Live Social Semantics (LSS)<br />Stack<br />Architecture<br />Contact Tracking<br />RDF for Contact Data<br />RDF for Tagging Data<br />Integration of Personal Data<br />Connection modalities<br />TAGora Sense Repository<br />Profile Building<br />Visualization<br />Deployment results<br />3<br />
    4. Semantics, sensors and the social web: Motivation<br />Networking: crucial component of conference activities<br />Conference organizers are keen to enhance the social experience<br /> Matchmaking Services<br />Enhanced by Interests that trascend scientific domain<br />4<br />
    5. Semantics, sensors and the social web: State of The Art<br />Opportunistic networking for mobile devices<br />Investigate interplay of networking and social contact<br />Sensing organizational aspects<br />Statsistical property of human mobility and contact<br />RFID to track conference attendees (IBM)<br />Sociopatterns: distributed RFID platform to detect human face-to-face contact<br /><ul><li>No real time mesh up of </li></ul>real-world face-to-face contact <br />online data from semantic social networking systems<br /><ul><li>Small number of participants
    6. No face-to-face contact detection</li></ul>5<br /><ul><li>No social no semantics</li></li></ul><li>Semantics, sensors and the social web: Live Social Semantics (LSS)<br />Users asked to (optionally)<br />Put a RFID tag on their badges<br />Register on LSS website providing<br />RFID ID number<br />Delicious, FlickR, LastFM account names<br />Activate a Facebook app to collect social contacts from there<br />6<br /><ul><li> Integration of Personal Data
    7. Detect connection among people based on
    8. Online social networks
    9. Topics of interest
    10. Realtime updated real-world social network</li></ul>http://www.sciencegallery.com/infectious<br />
    11. Semantics, sensors and the social web: Live Social Semantics - stack<br />Live Social Semantics<br />Web2.0<br />Linked Data<br />Real World<br />Delicious<br />semanticweb.org<br />rkbexplorer.com<br />acm, dblp, citeseer …<br />7<br />
    12. Semantics, sensors and the social web: Live Social Semantics - Architecture<br />8<br />4store<br />Aggregator<br />Local Server<br />Social Semantics<br />Real World<br />RDF Cache<br />Real World Contact Data<br />RFID Badges<br />
    13. Semantics, sensors and the social web: LSS Acrhitecture - Contact Tracking<br />Local Server<br />Multi-channel bi-directional radio communication<br />Exchange of low-power signals shielded by the human body <br />Contacts recorded only if “in-front-of” position detected<br />UDP packets from RFID readers<br />To a central server<br />Forwarded to a post-processing server<br />Instantaneous contact graph<br />Cumulative proximity relation weighted graph<br />9<br />
    14. Semantics, sensors and the social web: LSS Architecture – RDF for Contact Data<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1410<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1515<br />hasPhysicalContact<br />contactWith<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/contact/day3/1410/1515<br />contactDate<br />"2009-06-03"^^<http://www.w3.org/2001/XMLSchema#date><br />contactDuration<br />"00:01:43"^^<http://www.w3.org/2001/XMLSchema#time> <br />10<br />10<br />
    15. Semantics, sensors and the social web: LSS Architecture<br />11<br />COP + Publications<br />Profile<br />Builder<br />dbtune.org<br />RKBExplorer.com<br />Publications<br />dbpedia.org<br />data.semanticweb.org<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 />Aggregator<br />Local Server<br />Social Semantics<br />Real World<br />RDF Cache<br />Real World Contact Data<br />RFID Badges<br />
    16. Semantics, sensors and the social web: LSS Architecture – Profile Builder<br />1212<br />Web2.0<br />Linked Data<br />LastFM artists<br />semanticweb.org<br />rkbexplorer.com<br />Delicious<br />acm, dblp, citeseer …<br />DBtune<br />TAGora tagging ontology<br />(Extractor Daemon)<br />Tag<br />Dpedia<br />TAGora Sense Repository<br />
    17. Semantics, sensors and the social web: LSS - Integration of Personal Data (1/2)<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/foaf/1<br />Martin<br />Szomszor<br />Delicious Tagging and Network<br />RFID Contact Data<br />http://tagora.ecs.soton.ac.uk/delicious/martinszomszor<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/1410<br />Flickr Tagging and Contacts<br />Conference Publication Data<br />http://tagora.ecs.soton.ac.uk/flickr/7214044@N08@N08<br />http://data.semanticweb.org/person/martin-szomszor/<br />Lastfm favourite artists and friends<br />Past Publications, Projects, Communities of Practice<br />http://tagora.ecs.soton.ac.uk/lastfm/count-bassy<br />http://southampton.rkbexplorer.com/id/person-05877<br />Facebook contacts<br />http://tagora.ecs.soton.ac.uk/facebook/613077109<br />13<br />
    18. Contact data, FB friends, Delicious tags etc each stored in distinct graphs<br />Advanatges:<br />Approximates a distributed Linked Data scenario<br />Different processes can update the data model asynchronously<br />Push/Pull whenever from wherever to the visualization client<br />14<br />Semantics, sensors and the social web: LSS - Integration of Personal Data (2/2)<br />14<br />
    19. Semantics, sensors and the social web: LSS – connection modalities<br />15<br />Delicious<br />Folksonomies, The Semantic Web, and Movie Recommendation<br />CiroCattuto<br />Martin<br />Szomszor<br />Live Social Semantics<br />Publications<br />www.tagora-project.eu<br />Projects<br />15<br />
    20. Semantics, sensors and the social web: LSS - RDF for Tagging Data<br />isFilteredTo<br />didYouMean<br />GlobalTag<br />hasGlobalFrequency<br />xsd:integer<br />Tag<br />rdfs:label<br />DomainTag<br />xsd:string<br />hasDomainFrequency<br />xsd:integer<br />hasGlobalTag<br />UserTag<br />hasNextSegment (f)<br />hasUserFrequency<br />xsd:integer<br />hasDomainTag<br />TagSegment<br />usesTag<br />segmentTag (f)<br />tagAssigned<br />FinalTagSegment<br />hasTagSequence (f)<br />hasPost<br />Tagger<br />Post<br />http://tagora.ecs.soton.ac.uk/schemas/tagging#<br />taggedResource<br />xsd:dateTime<br />http://www.w3.org/2001/XMLSchema#<br />subclass<br />property<br />taggedOn<br />Resource<br />(f) = functional property<br />16<br />
    21. Semantics, sensors and the social web: LSS - TAGora Sense Repository (1/4)<br />Tag filtering service + metadata about tags and their possible senses (SPARQL endpoint, REST API)<br />Resource Index creation<br />XML dump of all Wikipedia pages<br /> title, redirection links, disambiguation links, keywords and their frequencies<br />For each page it stores list of all incoming links + total links<br />Link to Dbpedia by owl:sameAs<br />17<br />17<br />
    22. Semantics, sensors and the social web: LSS - TAGora Sense repository (2/4)<br />Search for senses (of DBpedia resources)<br />Search against resource titles + redirection and/or disambiguation links<br />Weight of sense “r” for tag “T”: #incomingLinksR / #incomingLinkAllSensesForT<br />Senses associated with general concepts receive higher weight<br />Selected sense = Global Tag in TSR associated with the User Tag in LSS <br />More than 1 sense exists  Tag Disambiguation<br />18<br />18<br />
    23. Semantics, sensors and the social web: LSS – TAGora Sense Repository (3/4)<br />TAGora Sense Repository<br />tagging:hasGlobalTag<br />tagging:GlobalTag<br />tagging:UserTag<br />http://tagora.ecs.soton.ac.uk/tag/ontologymapping<br />http://tagora.ecs.soton.ac.uk/delicious/tag/ontologymapping<br />disam:hasPossibleSense<br />http://dbpedia.org/resource/Semantic_Integration<br />tagging:UsesTag<br />tagging:Tagger<br />foaf:Person<br />http://tagora.ecs.soton.ac.uk/delicious/martinszomszor<br />foaf:interest<br />foaf:Person<br />http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/eswc2009/foaf/1<br />owl:SameAs<br />19<br />
    24. Semantics, sensors and the social web: LSS - TAGora Sense Repository (4/4)<br />Tag disambiguation<br />Term Vector = Context = other tags used to annotate the same resource by the same user<br />Term frequency vector = frequencies of keywords in the given sense (Dbpedia resource)<br />Cosine similarity<br /> candidate resource list of interest C<br />20<br />apple, film, 1980, ..<br />apple, inc, computer, ..<br />apple, iphone, computer, ..<br />apple, tree, fruit, ..<br />20<br />
    25. Semantics, sensors and the social web: LSS - Profile Building (1/2)<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 ; <br />fr = total frequency of all tags disambiguated to sense r<br />ur = time decay factor = ⌈days(r)/90⌉<br />3) Create Interest List<br />If more than 50 interests are suggested, rank by weight and suggest the top 50<br />Users must verify the list before it is published<br />21<br />
    26. Semantics, sensors and the social web: LSS – Profile Building (2/2)<br />Interest list publishing must be approved by users<br />22<br />
    27. Semantics, sensors and the social web: LSS - Visualization (1/2)<br />Spatial View<br />Accessible from publicly exposed main monitor<br />Participans: yellow disc / FB picture<br />Contacts: yellow edges<br />Weight of contacts: edge thickness and opacity<br />Type of contacts: edges decorated by online sources icons<br />RFID readers: labelled grey shapes<br />Coarse-grained localization of participants<br />23<br />23<br />
    28. Semantics, sensors and the social web: LSS – Visualization (2/2)<br />User-focus view<br />Accessible from any web-browser<br />W = Ongoing + cumulative contacts for a given user<br />Close relevant triangles: contacts linked to both the given user and any other one<br />Subsection of neighbourhood that is relevant for user networking at the moment<br />24<br />24<br />
    29. Semantics, sensors and the social web: Deployments<br />European Semantic Web Conference (ESWC2009)<br />Attendees 305<br />187 Participated in LSS<br />139 of them registered online<br />Hypertext (2009)<br />Attendees 150<br />113 Participated in LSS<br />97 of them registered online<br />Totals<br />455 Attendees<br />300 Participated in LSS<br />236 registered online<br />21% people took a badge but did not register<br />25<br />
    30. Semantics, sensors and the social web: Deployment Results <br />Declaration of SNS Accounts<br />26<br />
    31. Profiles of Interest<br />Semantics, sensors and the social web: Deployment Results<br />27<br />
    32. Accuracy of DBPedia Senses<br />Semantics, sensors and the social web: Deployment Results<br />28<br />
    33. Survey Results<br />Semantics, sensors and the social web: Deployment Results<br />Why some users did register on the LSS site but did not enter any social networking account:<br />29<br />
    34. Semantics, sensors and the social web: Future Work<br />Allow individuals to link to their own foaf profiles<br />More SNS sites:<br />i.e. Twitter, LinkedIn<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 />Align Tagging Ontology with SIOC<br />30<br />
    35. Semantics, sensors and the social web: Conclusions<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, Last.fm, Delicious, etc, I can extrapolate the interest Online_Social_Networks<br />31<br />
    36. Social dynamics in conferences: Paper Details<br />Title: “Social dynamics in conferences: analyses of data from the Live Social Semantics application”<br />Authors<br />Martin Szomszor- City eHealth Research Centre, UK<br />CiroCattuto- ISI Foundation, Turin, Italy<br />Wouter Van den Broeck - ISI Foundation, Turin, Italy<br />Alain Barrat - Centre de Physique Théorique, Marseille, France<br />HarithAlani - Knowledge Media Institute, The Open University, UK<br />Year<br />2010<br />Conferences<br />International Semantic Web Conference (ISWC2010)<br />32<br />
    37. Social dynamics in conferences: Overview<br />Motivation<br />Analysis description<br />Analysis results<br />F2F interaction<br />Frequent users<br />Senior users<br />Online vs offline popularity<br />Netwoking with online and offline friends<br />Discussion and Future Work<br />Personal Remarks<br />33<br />
    38. Social dynamics in conferences: Motivation<br />Correlation among features of those users which are connected in a social network<br />Long-standing problem in social science, ecology and epidemiology<br />“Assortative Mixing” pattern: tendency of network nodes to link with others having similar properties<br />LSS deployments results are analyzed <br />Purpose: <br />novel insights into the comparability of online and offline networks<br />Better understand impact of specific parameters on the social contact behaviour of individuals and groups<br />34<br />34<br />
    39. Social dynamics in conferences: Analysis description (1/2)<br />Face-to-face interactions in scientific conferences<br />Contacts frequency and duration compared across the 3 deployments<br />Networking behaviour of frequent users<br />Consider only users who participated in 2 deployments quantitatively and qualitatively, compared with one-time users<br />Scientific seniority of users<br />Correlation among seniority of users and seniority of their F2F contacts<br />General strenght of seniority user’s social network<br />Correlation among seniority of users and # of their Twitter followers<br />35<br />35<br />
    40. Social dynamics in conferences: Analysis description (2/2)<br />Comparison of F2F contact network with Twitter and Facebook<br />Are people with strong online social presence very active even in F2F networking? And vice versa<br />Social networking with online and offline friends<br />Contact networks analyzed considering co-authorship and online social networking relationships<br />36<br />36<br />
    41. Social dynamics in conferences: Analysis Result <br />37<br />37<br />
    42. Social dynamics in conferences: Analysis Result<br />Returning attendees have larger average interaction time and frequency, especially among each other<br />38<br />38<br />
    43. Social dynamics in conferences: Analysis Result<br />39<br />During different conferences<br />People interacted with different contacts<br />Time spent in these interaction is very similar<br />39<br />
    44. Social dynamics in conferences: Analysis Result<br />40<br />People tend to interact with those who have similar seniority level<br /><ul><li>Scientific seniority definition se(u)
    45. # papers authored at semantic web related conferences
    46. H-index from scholarometer.indiana.edu</li></ul>40<br />
    47. Social dynamics in conferences: Analysis Result<br />Seniority and Social Activity<br />Interact with more distinct people<br />Spend more time in F2F interactions <br />Higher amount of interactions<br />41<br />41<br />
    48. Social dynamics in conferences: Analysis Result<br />42<br />Most senior scientists are not the mostly followed on Twitter<br /><ul><li>First two peaks: researchers with high visibility who chaired in other conferences
    49. Third peak: developer</li></ul>42<br />
    50. Social dynamics in conferences: Analysis Result<br />43<br />People most active in F2F contacts do not necessarily have the largest online social network<br />43<br />
    51. Social dynamics in conferences: Analysis Result<br />People sharing an online or professional link meet more often <br />The average number of encounters and the total time spent in interaction is higher for co-authors<br />44<br />44<br />
    52. Social dynamics in conferences: Conclusions and Future Work<br />Behaviour in F2F networking is very similar across events<br />Limitation: only people who used LSS were considered<br />Future work: RFID tags with on-board memory to enable F2F contacts to be logged regardless of distance from RFID readers<br />Future work: Other possible parameters i.e. age, affiliations, chronology of social relationships etc.<br />Future work: Consider account date of creation + whether user is active over there<br />45<br />45<br />
    53. Personal Remarks<br />User profile data integration <br />usefully enhanced by real-life ongoing interactions<br />Not yet taken advantage from semantic representation neither of tags nor of contacts<br />Tag hierarchy from Dbpedia concepts to find more specific topics to refine contact recommendation based on similar topics<br />SPARQL endpoint on contact data<br />Dbpedia descriptions demonstrated to not being enoughly accurate<br />Not enough justification of contact recommendations by i.e. listing common topics of interest<br />Absence of a Privacy Manager at a triple level<br />46<br />46<br />

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