Parallel Identities for Managing Open Government Data

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Abstract:

The widespread availability of Open Government Data is exposing significant challenges to trust in its unplanned applications. As data are accumulated, transformed, and presented through a chain of independent third parties, there is a growing need for sophisticated models of provenance. Significant progress has been made in describing data derivation, but has been limited by its ability to distinguish between transformations that change content and transformations that simply change representation. We have found that Functional Requirements for Bibliographic Resources (FRBR) can, when paired with a derivational provenance model like the World Wide Web Consortiumtextquoteright{}s emerging PROV standard, successfully represent web resource accession, distinguish between transformations of content and format, and facilitate veracity using cryptographic digests. We show how cryptographic digest algorithms can be used to provide an automated method and tools for the coordination of multiscale identity of information resources using FRBR concepts and cryptographic digests.

History

DateCreated ByLink
February 7, 2012
15:14:09
James McCuskerDownload

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.
Linking open government dataLinking Open Government Data (LOGD)
Principal Investigator: Jim Hendler and Deborah L. McGuinness
Description: The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on Data.gov and other websites.
Population Science Grid (PopSciGrid)
Principal Investigator: Deborah L. McGuinness
Description: The National Cancer Institute’s (NCI) PopSciGrid Community Health Portal is an evolving platform demonstrating how health behavior, policy, and demographic data can be integrated, visualized, and communicated to empower communities and support new avenues of research and policy for cancer prevention and control.
SSIII Project LogoSemantic Sea Ice Interoperability Initiative (SSIII)
Principal Investigator: Siri Jodha Singh Khalsa, Mark Parsons, and Ruth Duerr
Co Investigator: Peter Fox and Deborah L. McGuinness
Description: SSIII is a National Science Foundation (NSF) funded effort to enhance the interoperability of sea ice data to establish a network of practitioners working to enhance semantic interoperability of all Arctic data. SSIII is a collaborative project between NSIDC and the Rensselaer Polytechnic Institute (RPI) Tetherless World Constellation project. We seek to build on the work initiated under the International Polar Year (IPY) and create a community of practice working to improve interoperability within the Polar Information Commons (PIC), the Sustained Arctic Observing Network (SAON), and broader global systems.

Related Research Areas:

Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts: Semantic Web
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web
Semantic eScience
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.

As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.

Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: