Capturing provenance of global change information

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Abstract:

Global change information demands access to data sources and well-documented provenance to provide the evidence needed to build confidence in scientific conclusions and decision making. A new generation of web technology, the Semantic Web, provides tools for that purpose.

Related Projects:

Global Change Information System: Information Model and Semantic Application Prototypes (GCIS-IMSAP)
Principal Investigator: Peter Fox
Description: The Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) proposes to facilitate the vocabulary and ontology development within the context of the overall development of semantic prototypes for the National Climate Assessment (NCA) portals using a combination of environmental inter-agency collaborations in a use-case focused workshop setting, information modeling, and software developments and deployments. The prototypes are intended to provide search and browse options that inspire confidence that all relevant information has been found; data providers will be citable with detailed provenance generation. Expected deliverables are: information models, vocabulary and ontology services for vetted climate assessment settings, and search/ browse prototypes.

Related Research Areas:

Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
Ontology Engineering Environments
Lead Professor: Deborah L. McGuinness
Description: Ontology Engineering Environments
Concepts:
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: eScience