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.
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.