TPS Trust and Provenance in Sweto

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TPSTrust and Provenance in Sweto

• Meenakshi Nagarajan
• Willie Milnor
• Nicole Oldham

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Introduction

• Nature of the Semantic Web
• Machine understandable information
• Open, distributed, low barriers with publication
• New techniques to validate information

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• 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

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• 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
  Peoples leader made by CNN and Iraq Daily.

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• Option 2 May be, a trust value for every source
  for every domain under consideration
• Infinite domains and sources not scalable
• Option 3
• Possibility of 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

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• Very close to humans analyze content to ascertain
  credibility
• Recommendation systems, e-Bay etc
• TPS
• Trust a 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.

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have beliefs in
User
trusts
Belief in statement
trusts
Ux
trusts
trusts
trusts
trusts
trusts
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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

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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 informationhttp//www.ksl.stanford.edu
  /people/pp/papers/PinheirodaSilva_DEBULL_2003.pdf

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• Role of trust in ascertaining credibility of
  information Web of trusthttp//www.cs.washingto
  n.edu/homes/pedrod/papers/iswc03.pdf
• A framework for trust propagation using notions
  of trust and distrust in a web of trust
  e-commerce systemshttp//tap.stanford.edu/trust04
  .pdf

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• 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 provenancehttp//ebiquity.umb
  c.edu/v2.1/_file_directory_/resources/58.pdf

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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

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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

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• 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
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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

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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)

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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.

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Representation of Trust in the WOT

• A matrix that contains the actual trust values
  that each of the n users placed in any of the
  other users is maintained.
• ti is the row representing the trust that user i
  has for each of the other users. User i can
  specify trust tik for any user k.
• If user i does not trust user k then tik 0.
  tik ? tki.

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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
• The trust value tik will be
• recalculated as the trust
• values change for any of
• the users.

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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

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Trust in a semantic association

• Calculating trust in individual statements
• Calculating trust in the association

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• 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)

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TIPS Architecture
Web Interface
Query processor (SemDis)
Trust ranking module
SWETO
Beliefs
Trust aggregator
WOT
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Schema
WOT
Beliefs
truth_ value
user
user
trusts
with_probability
to_degree
stmt
user
believed_by
trust_ value
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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

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Test Cases

• A user requests both unranked and then ranked
  results for the same query.
• Unranked results appear in order found.
• A user adds an explicit truth value to a
  statement in an association.
• All corresponding associations are affected
• Some may be now have different ranks
• A users changes/states and explicit trust in a
  believer of a statement.
• Corresponding associations are affected
• Some now have different ranks

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References

• http//lsdis.cs.uga.edu/library/download/SAA2004-
  PISTA.pdf
• http//ebiquity.umbc.edu/v2.1/_file_directory_/res
  ources/58.pdf
• http//www.eil.utoronto.ca/km/papers/fox-kp1.pdf
• http//www.ksl.stanford.edu/people/pp/papers/Pinhe
  irodaSilva_DEBULL_2003.pdf
• http//www.cs.washington.edu/homes/pedrod/papers/i
  swc03.pdf
• http//tap.stanford.edu/trust04.pdf
• http//trust.mindswap.org/
• http//lsdis.cs.uga.edu/projects/SemDis/Sweto/swet
  o.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/iswcRanki
  ng2004.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.pd
  f