Post on 31-Dec-2015
description
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
iTrust 2005, INRIA, France
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
iTrust 2005, INRIA, France
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
iTrust 2005, INRIA, France
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)
iTrust 2005, INRIA, France
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
iTrust 2005, INRIA, France
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