iwtrust: improving user trust in answers from the web

IWTrust:

Improving User Trust in

Answers from the Web

Ilya ZaihrayeuITC-IRST

Paulo Pinheiro da SilvaDeborah L. McGuinness

Stanford University

iTrust 2005, INRIA, France

Trusting Answers It may be challenging for a user to establish a degree of trust,

untrust, mistrust and distrust in answers if the answers are provided without any kind of justification

Knowledge Provenance (KP) is a description of both the origins of knowledge and the reasoning process to produce an answer

Users may need KP to establish a degree of trust in the answer Which sources were used? Who are the authors of each sources? Which engines (i.e., agents) were used? What are the assumptions of each engine? Are the engines’ rules

sound? KP itself may not be enough for trusting the answer

I may know nothing about one or more of the sources in the KP I may have no information about the reliability of one or more of

then engines in the KP


Trusting Answers from the Web The overall process of establishing a degree of trust in

answers from web applications is particularly complex since applications may rely on: Hybrid and distributed processing, e.g., web services, the Grid Large number of heterogeneous, distributed information sources,

e.g., the Web information sources with more variation in their reliability, e.g.,

information extraction Sophisticated information integration methods, e.g., SIMS,

TSIMMIS The definition of trust is a significant part of the process

The task of keeping, encoding, sharing and gathering KP for answers is another part of the process

The use of KP to derive trust values for answers is yet another part of the process


The Inference Web

A1IE1

S2

IE2

PML Documents IWBase

A2

An

...

Q(U1)

S1

S3...

The Inference Web is an infrastructure supporting explanations for answers from the web The Proof Markup Language (PML) is used to encode answer

justification, i.e., information manipulation traces, proofs IWBase is used to annotate PML documents with proof-related

data, i.e., trust values for sources and engines User U1 asks question Q

{A1,A2,…,An} is an answer set for Q


Inference Web and KP Inference Web supports KP for answers derived by

multiple methods Information extraction – IBM (UIMA), Stanford (TAP) Information integration – USC ISI (Prometheus/Mediator); Rutgers

University (Prolog/Datalog) Task processing – SRI International (SPARK) Theorem proving

First-Order Theorem Provers –SRI International (SNARK); Stanford (JTP); University of Texas, Austin (KM)

SATisfiability Solvers – University of Trento (J-SAT) Expert Systems – University of Fortaleza (JEOPS)

Service composition – Stanford, University of Toronto, UCSF (SDS) Semantic matching – University of Trento (S-Match) Debugging ontologies – University of Maryland, College Park

(SWOOP/Pellet) Problem solving – University of Fortaleza (ExpertCop)


The Inference Web Trust (IWTrust)

(A1, t11, t12,...)IE1

S2

IE2

IW Trust Framework

PML Documents IWBase

(A2, t21, t22,...)

(An, tn1, tn2,...)

...

Q(U1)

S1

S3...

S4

IW TrustNet

u4

u7 u6

u3

u5u1

t1-5

t5-6

t6-7

t6-3

t1-3

t3-4

t7-S1

t7-IE1

t4-S4

t4-S3

t1-IE2

IWTrust extends the Inference Web to support trust computation IW TrustNet is a social network of recommenders A component computing trust values for answers

Trust values are used to rank answers and answer justifications User U1 trusts U3 to a degree t1-3


http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.html

Conclusions IWTrust provides infrastructure for building a trust graph

from users asking questions to answers Knowledge provenance is a key element of the trust

graph and a requirement for trusting answers in general Inference Web is a Semantic Web solution for

knowledge provenance

iw.stanford.edu

Inference Web is a solution for the Semantic Web proof layer

IWTrust intends to be a solution for the Semantic Web trust layer