TPSTrust and Provenance in Sweto

Meenakshi Nagarajan

Willie Milnor

Nicole Oldham

Introduction

Nature of the Semantic Web Machine understandable informationOpen, distributed, low barriers with publicationNew 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 sourceCNN = 0.9

Counter-Intuitive to how we process informationStatement 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: Possibility of finite users ascertaining their

confidence in some statementsTrust 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

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.

statement

User

have beliefs in

truststrusts

trusts

trusts

Belief in statement

trusts

Ux

trusts

trusts

Based on this..

We identified the requirement of two modelsProvenance 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 informationhttp://www.ksl.stanford.edu/people/pp/papers/PinheirodaSilva_DEBULL_2003.pdf

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

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

The Models ..

Provenance Model – enhancing SwetoCaptures

Provenance information of statements in Sweto Confidence / truth value of a statement User who placed that confidence / truth value

The Models ..

Trust Model WOTCaptures

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 statementDepending 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.

A

B

E

C

D

F

0.7

0.2

0

1.0

0.6

0.7

0.8

0.4

0.3

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.

uA uB uC uD uE uF

tA 1.0 .7 1.0

tB 1.0 0

tC 1.0 .6

tD .2 .7 1.0 .4

tE .8 1.0

tF .3 1.0

A

B

E

C

D

F

0.7

0.2

0

1.0

0.6

0.7

0.8

0.4

0.3

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 .6A C D = .6

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

uA uB uC uD uE uF

tA 1.0 .7 1.0 .6 .072 .24

tB 0 1.0 0 0 0 0

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 associationOnly as strong as its weakest link. The value of its least trustworthy component.

(statement)

TIPS Architecture

WOT

SWETOBeliefs

Trustaggregator

Queryprocessor(SemDis)

Web Interface

Trust ranking module

Schema

user usertrusts

WOT

trust_value

to_degree

stmt userbelieved_by

truth_value

with_probability

Beliefs

Test Set

Small/manageable set of SWETO instances

Synthetically generated 15 WOT usersAdded corresponding nodes to the graphGenerated synthetic trust relationships

Random values between 0 and 1

Synthetically generated statements of truthRandom 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 affected2. 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 affected2. 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