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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
      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
    • 5. Semantics, sensors and the social web: State of The Art
      Opportunistic networking for mobile devices
      Investigate interplay of networking and social contact
      Sensing organizational aspects
      Statsistical property of human mobility and contact
      RFID to track conference attendees (IBM)
      Sociopatterns: distributed RFID platform to detect human face-to-face contact
      • No real time mesh up of
      real-world face-to-face contact
      online data from semantic social networking systems
      • Small number of participants
      • 6. No face-to-face contact detection
      5
      • No social no semantics
    • Semantics, sensors and the social web: Live Social Semantics (LSS)
      Users asked to (optionally)
      Put a RFID tag on their badges
      Register on LSS website providing
      RFID ID number
      Delicious, FlickR, LastFM account names
      Activate a Facebook app to collect social contacts from there
      6
      • 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
      http://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
    • 44. Social dynamics in conferences: Analysis Result
      40
      People tend to interact with those who have similar seniority level
      • Scientific seniority definition se(u)
      • 45. # papers authored at semantic web related conferences
      • 46. H-index from scholarometer.indiana.edu
      40
    • 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
    • 48. Social dynamics in conferences: Analysis Result
      42
      Most senior scientists are not the mostly followed on Twitter
      • First two peaks: researchers with high visibility who chaired in other conferences
      • 49. Third peak: developer
      42
    • 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

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