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					TPS
Trust and Provenance in
Sweto

Meenakshi Nagarajan
Willie Milnor
Nicole Oldham
Introduction
   Nature of the Semantic Web
     Machine  understandable information
     Open, distributed, low barriers with publication
     New techniques to validate information
 Provenance is key to establishing trust in
  the information
 Not adequate to associate trust in the
  content of the source
 Unreasonable to know trust in every
  statement by verifying provenance and
  source
   Option1: Associate a trust value with every
    source
     CNN   = 0.9
   Counter-Intuitive to how we process
    information
     Statement  about “War in Iraq” and “The Iraqi
      People’s leader” made by CNN and Iraq
      Daily.
   Option 2: May be, a trust value for every
    source for every domain under
    consideration
     Infinite   domains and sources – not scalable
   Option 3:
     Possibilityof finite users ascertaining their
      confidence in some statements
     Trust anyone has on a statement as a
      function of their trust on the user who placed
      a confidence on this statement
 Very close to humans analyze content to
  ascertain credibility
 Recommendation systems, e-Bay etc
 TPS
     Trusta member of a network can associate
     with a statement on the Semantic Web is
     proportional to the belief asserted on the
     statement by some user (also in the network)
     and the trust the member has on this user.
                                       statement
                   have
                   beliefs in
  User            trusts                   Belief in statement
trusts
                                                 Ux

              trusts                    trusts
                           trusts


         trusts               trusts
Based on this..
   We identified the requirement of two
    models
     Provenance      model (essentially Sweto itself)
          Provenance information of statements
     Trust   model
          Trust between users who placed a confidence
           value in a statement in Sweto
Related Work
 Knowledge management to determine the
  validity and origin of information on the
  web
  http://www.eil.utoronto.ca/km/papers/fox-
  kp1.pdf
 Proof-like support system for explaining
  provenance information
  http://www.ksl.stanford.edu/people/pp/pap
  ers/PinheirodaSilva_DEBULL_2003.pdf
   Role of trust in ascertaining credibility of
    information – Web of trust
    http://www.cs.washington.edu/homes/pe
    drod/papers/iswc03.pdf
   A framework for trust propagation using
    notions of trust and distrust in a web of
    trust – e-commerce systems
    http://tap.stanford.edu/trust04.pdf
   Issues related to using trust in web
    based social networks, specifically in
    building and maintaining a trust network
    on the web
    http://trust.mindswap.org/
   Combining trust and provenance
    http://ebiquity.umbc.edu/v2.1/_file_direct
    ory_/resources/58.pdf
The Models ..
   Provenance Model – enhancing Sweto
     Captures
       Provenance information of statements in Sweto
       Confidence / truth value of a statement

       User who placed that confidence / truth value
The Models ..
   Trust Model WOT
     Captures
       Trust between users, where a user E users who
        entered a confidence / truth value in a statement
       When a user enters a confidence / truth value into
        the provenance model, he is
           Added to the provenance model
           Optionally, he could add himself to the WOT if he wishes
            to place trust values in other users
    Placing trust in other users of the WOT
      intuitively,
                 user1 verifies the confidence value
       placed by userx in the statement
      Depending on the confidence values, user1
       establishes trust in userx




                      A BIG ASSUMPTION
ALL USERS ARE BASICALLY TRUSTWORTHY AS FAR AS GOING THROUGH
       THE PROCESS OF ENTERING TRUTH AND TRUST VALUES
Unique features and contribution

   Features
     Source   and domain consideration. No single source,
      single trust value concept
     Personalized trust metrics for every user in the
      system – respecting the subjective nature of trust
     Adaptive model
          Ability to change trust in users and/or truth values on
           statements
          Immediately reflects on results obtained
Aggregation in TPS
   Primary Question we are trying to answer
        How much can I trust an association I get
        from Sweto ?
   Can also answer
     How    much do I trust user x ? (directly or
        through propagation of trust / distrust)
Web Of Trust
   A directed Graph of users of the system with edge
    weights as the trust values between them.

   Every user who places a truth value in an assertion is
    represented as a node in this graph.
                                    B         0
                    0.7
                                                        E
                A                       0.7
                              0.2
                                                  0.8       0.3
                 1.0                      D
                                              0.4
                          C     0.6                         F
Representation of Trust in the WOT
   A matrix that contains the                    uA     uB        uC       uD         uE        uF
    actual trust values that each of        tA    1.0    .7        1.0
    the n users placed in any of
                                            tB           1.0                           0
    the other users is maintained.
                                            tC                     1.0      .6
   ti is the row representing the                .2     .7                 1.0                  .4
                                            tD
    trust that user i has for each of
    the other users. User i can             tE                              .8         1.0
                                                               B            0
    specify trust tik for any user k.       0.7
                                            tF                                         .3
                                                                                        E        1.0
                                        A                          0.7
   If user i does not trust user k                     0.2
    then tik = 0. tik ≠ tki.                                                     0.8         0.3
                                        1.0                             D
                                                                            0.4
                                                   C      0.6                                F
Propagation of Trust in the WOT
   The trust will then be propagated throughout the WOT to obtain a matrix
    that contains trust values for all users.

   The trust value associated with each path is calculated by applying a
    concatenation function to multiply the trusts along the path. For example,
    tik * tkj is the amount that user i trusts user j via k.

    ABED              = 0         Aggregate Maximum for tAD is .6
    A  C D             = .6
                                                    uA     uB     uC    uD     uE    uF
   The trust value tik will be
                                              tA   1.0     .7    1.0    .6    .072   .24
    recalculated as the trust
    values change for any of                  tB    0     1.0     0     0      0     0

    the users.                                tC   .12    .42    1.0    .6    .072   .24

                                              tD    .2     .7     .2    1.0   .12    .4

                                              tE   .16    .55    .16    .8    1.0    .32

                                              tF   .048   .168   .048   .24    .3    1.0
Trust in a semantic association

 Trust on a statement function of truth
  value on the statement and trust on user
  who placed this truth value
 Extending this to a semantic association –
  function of trusts on individual statements
Trust in a semantic association

 Calculating trust in individual statements
 Calculating trust in the association
 User X
 Calculating trust in a statement S
     More  than one user can place a truth value on
      a statement
     Trust in S = truth value placed on S by user
      that user X trusts the most
   Calculating trust in a semantic association
     Only  as strong as its weakest link.
     The value of its least trustworthy component.
      (statement)
TIPS Architecture
                      Web Interface




        Trust ranking module            Query
                                      processor
                                      (SemDis)
      Trust
    aggregator

                          Beliefs     SWETO
      WOT
Schema
          WOT                         Beliefs


               trusts                  truth_
 user                   user           value



                                            with_probability

   to_degree


                                      believed_by
               trust_          stmt                   user
               value
Test Set
 Small/manageable set of SWETO
  instances
 Synthetically generated 15 WOT users
     Added corresponding nodes to the graph
     Generated synthetic trust relationships
          Random values between 0 and 1
   Synthetically generated statements of truth
     Random     values between 0 and 1
Test Cases
1.    A user requests both unranked and then ranked
      results for the same query.
     1.   Unranked results appear in order found.
2.    A user adds an explicit truth value to a statement in an
      association.
     1.   All corresponding associations are affected
     2.   Some may be now have different ranks
3.    A users changes/states and explicit trust in a believer
      of a statement.
     1.   Corresponding associations are affected
     2.   Some now have different ranks
References
    http://lsdis.cs.uga.edu/library/download/SAA+2004-PISTA.pdf
    http://ebiquity.umbc.edu/v2.1/_file_directory_/resources/58.pdf
    http://www.eil.utoronto.ca/km/papers/fox-kp1.pdf
    http://www.ksl.stanford.edu/people/pp/papers/PinheirodaSilva_DEBULL_2003.pdf
    http://www.cs.washington.edu/homes/pedrod/papers/iswc03.pdf
    http://tap.stanford.edu/trust04.pdf
    http://trust.mindswap.org/
    http://lsdis.cs.uga.edu/projects/SemDis/Sweto/sweto.pdf
    http://lsdis.cs.uga.edu/projects/SemDis/
    http://lsdis.cs.uga.edu/lib/download/AS03-WWW.pdf
    http://lsdis.cs.uga.edu/library/download/iswcRanking2004.pdf
    http://tap.stanford.edu/trust04.pdf
    http://www.cs.cornell.edu/home/kleinber/auth.pdf
    http://www.semagix.com/
    http://moloko.itc.it/paoloblog/papers/trust2004.pdf

				
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posted:10/23/2011
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