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IWTrust: Improving User Trust in Answers from the Web

IWTrust: Improving User Trust in Answers from the Web. Ilya Zaihrayeu ITC-IRST Paulo Pinheiro da Silva Deborah L. McGuinness Stanford University. Trusting Answers.

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IWTrust: Improving User Trust in Answers from the Web

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  1. IWTrust:Improving User Trust in Answers from the Web Ilya Zaihrayeu ITC-IRST Paulo Pinheiro da Silva Deborah L. McGuinness Stanford University

  2. 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

  3. 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

  4. The Inference Web • 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 PML Documents IWBase S1 A1 IE1 Q(U1) S2 A2 ... ... S3 An IE2

  5. 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)

  6. IW TrustNet t6-7 t7-S1 u7 u6 t7-IE1 t6-3 t4-S4 t3-4 u4 u3 S4 t5-6 t4-S3 t1-3 t1-5 t1-IE2 u1 u5 IW Trust Framework The Inference Web Trust (IWTrust) • 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 PML Documents IWBase S1 (A1, t11, t12,...) IE1 Q(U1) S2 (A2, t21, t22,...) ... ... S3 (An, tn1, tn2,...) IE2

  7. http://www.w3.org/2004/Talks/0412-RDF-functions/slide4-0.htmlhttp://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 • IWTrust intends to be a solution for the Semantic Web trust layer • Inference Web is a solution for the Semantic Web proof layer

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