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Inference Web: Enabling Accountable Web Applications

Inference Web: Enabling Accountable Web Applications. Deborah L. McGuinness 1 , Li Ding 1 , Paulo Pinheiro da Silva 2 , Cynthia Chang 1 , James Michaelis 1 , Jiao Tao 1 , Alyssa Glass 3 and Nicholas Del Rio 2

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Inference Web: Enabling Accountable Web Applications

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  1. Inference Web: Enabling Accountable Web Applications Deborah L. McGuinness1, Li Ding1, Paulo Pinheiro da Silva2, Cynthia Chang1, James Michaelis1, Jiao Tao1, Alyssa Glass3 and Nicholas Del Rio2 1 Rensselaer Polytechnic Institute 2 University of Texas El Paso3 Stanford University Inference Web Solution Why Inference Web? Example: trustworthiness ofWikipedia article using revision history • Interoperable Representation • to support different languages • to transcend system boundaries • Extensible Infrastructure • provides basic data services (search, validation, etc.) • supports end-user explanation • Low Adoption Cost • for data and ontology engineers • for application developers • for end users To trust answers from Webapplications, users need access to provenance metadata: i.e., who, what, when, where, how info. In 2006, our revision history analysis showed that the fragment in yellow color is less trustworthy. Is it trustworthy? 1 Use provenance (revision history) Inference Web (IW) provides infrastructure for representing and computing provenance on the Web and for the Web http://inference-web.org/ 2 3 End Users End-User Interaction services Data Access & Data Analysis Services Validate published PML data Explanation via Graph PML data on the Web Explanation via Summary Explanation via Annotation Access published PML data • IW: web service infrastructure • User accessible explanation UI • Off-the-shelf web services World Wide Web Enterprise Web D PML data PML data PML data PML data PML data PML data Enterprise Web D D • PML: provenance interlingua • Enhancing linked Web data • Modularized & customizable • Semantic Web based D D D D PML data D … Inference Web Infrastructure • Explaining task processing and machine learning for cognitive agents (CALO - RPI/Stanford/SRI) • Explaining collaborative problem-solving for integrated learners and reasoners (GILA - RPI/Lockheed Martin) • Explaining and integrating logic proofs produced by various logical reasoners (TPTP2PML - RPI/UTEP/Miami) • Logging and explaining transparent policy aware systems (TAMI/E2E - MIT/W3C/RPI/Stanford) • Supporting semantically-enabled scientific data access (VSTO/SPCDIS - UCAR/McGuinness Assoc) • Facilitating provenance-aware scientific data access via search (SPCDIS - UCAR/RPI/UTEP) • Explainable semantic discovery service (ESDS - Stanford) • Intelligence text-analytic toolkit (NIMD - Stanford / IBM / PNNL) Applications TW funding from:  DARPA, NSF, IARPA, ARL, Lockheed Martin, Fujitsu, SRI, IBM. June 11,2008

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