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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

Semantics, Sensors and the Social Web Presentation 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