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Explaining Conclusions from Diverse Knowledge Sources
J. William Murdock1, Deborah L. McGuinness2, Paulo Pinheiro da Silva3, Chris Welty1, and David Ferrucci1
1IBM Watson Research Center, 19 Skyline Drive, Hawthorn, NY 10532
2Knowledge Systems, Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305
3Department of Computer Science, The University of Texas at El Paso, 500 W University Ave, El Paso TX 79968-0518
Abstract. The ubiquitous non-semantic web includes a vast array of unstructured information such as HTML documents. The semantic web provides more structured knowledge such as hand-built ontologies and semantically aware databases. To leverage the full power of both the semantic and non-semantic portions of the web, software systems need to be able to reason over both kinds of information. Systems that use both structured and unstructured information face a significant challenge when trying to convince a user to believe their results: the sources and the kinds of reasoning that are applied to the sources are radically different in their nature and their reliability. Our work aims at explaining conclusions derived from a combination of structured and unstructured sources. We present our solution that provides an infrastructure capable of encoding justifications for conclusions in a single format. This integration provides an end-to-end description of the knowledge derivation process including access to text or HTML documents, descriptions of the analytic processes used for extraction, as well as descriptions of the ontologies and many kinds of information manipulation processes, including standard deduction. We produce unified traces of extraction and deduction processes in the Proof Markup Language (PML), an OWL-based formalism for encoding provenance for inferred information. We provide a browser for exploring PML and thus enabling a user to understand how some conclusion was reached.
LNCS 4273, p. 861 ff.
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© Springer-Verlag Berlin Heidelberg 2006