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TPS T rust and P rovenance in S weto

TPS T rust and P rovenance in S weto. 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.

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TPS T rust and P rovenance in S weto

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  1. TPSTrust and Provenance in Sweto Meenakshi Nagarajan Willie Milnor Nicole Oldham

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

  3. 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

  4. 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.

  5. 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

  6. 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.

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

  8. 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

  9. 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

  10. Role of trust in ascertaining credibility of information – Web of trusthttp://www.cs.washington.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

  11. 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.umbc.edu/v2.1/_file_directory_/resources/58.pdf

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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)

  17. 0 0.7 0.7 0.2 0.8 0.3 1.0 0.4 0.6 B E A D C F 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.

  18. 0 0.7 0.7 0.2 0.8 0.3 1.0 0.4 0.6 B E A D C F Representation of Trust in the WOT • A matrix that contains the actual trust values that each of the n usersplaced 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.

  19. 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 tADis .6 A  C D = .6 • The trust value tik will be recalculated as the trust values change for any of the users.

  20. 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

  21. Trust in a semantic association • Calculating trust in individual statements • Calculating trust in the association

  22. 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)

  23. TIPS Architecture Web Interface Trust ranking module Query processor (SemDis) Trust aggregator Beliefs SWETO WOT

  24. Schema WOT Beliefs truth_ value user user trusts with_probability to_degree stmt believed_by user trust_ value

  25. 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

  26. 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

  27. 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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