Technical Report - Abstracting Granular Provenance

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  1. Lebo, T. and McGuinness, D.L. (2010). Creating, Interpreting, and Repurposing Visual Messages. Tetherless World Constellation: Troy, NY. TWC-2010-2782



As Open Data becomes commonplace, methods are needed to integrate dis- parate data from a variety of sources. Although Linked Data design has promise for integrating world wide data, integrators often struggle to provide appropriate transparency for their sources and transformations. Without this transparency, cautious consumers are unlikely to find enough information to allow them to trust third party results. While capturing provenance in RPI’s Linking Open Government Data project, we were faced with the common problem that only a portion of provenance that is captured is effectively used. Using our water quality portal’s use case as an example, we argue that one key to enabling provenance use is a better treatment of provenance granularity. To address this challenge, we have designed an approach that supports deriving abstracted provenance from granular provenance in an open environment. We describe the approach, show how it addresses the naturally occurring unmet provenance needs in a fam- ily of applications, and describe how the approach addresses similar problems in open provenance and open data environments.

  1. @techreport{lebo_2012_creating-interpreting,
  2. Address = {Winslow Building, 2nd Floor 110 8th Street Troy, NY 12180},
  3. Author = {Timothy Lebo and Deborah L. McGuinness},
  4. Institution = {Rensselaer Polytechnic Institute},
  5. Month = {February},
  6. Number = {2782},
  7. Title = {Creating, Interpreting, and Repurposing Visual Messages},
  8. Year = {2010}}


DateCreated ByLink
April 22, 2012
Tim LeboDownload
April 18, 2012
Tim LeboDownload

Related Projects:

Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.