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A Distributed Knowledge Extraction Framework Based on Semantic Web Services
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| Project Summary
Key information technology advances in recent years include the emergence of distributed computing architectures based on web services; knowledge engineering efforts as evidenced by the development of science domain ontologies in the Semantic Web; and growing interest in scientific data mining as a means for automated knowledge extraction from the ever-increasing volumes of science observations and model data available. To facilitate exploitation of these promising techniques by the NASA science community, the University of Alabama in Huntsville (UAH) leads a multi-disciplinary, collaborative research team in proposing to prototype an extensible framework to integrate distributed resources including science data, related services, and the ontologies that describe them.
This project brings together a strong research team with a unique combination of skills and experience. Dr. Sara Graves' research center at UAH brings to the project ongoing data mining and semantics research to provide knowledge extraction, manipulation and analysis web services. The knowledge engineering aspects of this project wll capitalize on the long involvement in formal ontology design and development by Dr. Deborah McGuinness at Stanford University and McGuinness Associates. Dr. Peter Fox at the National Center for Atmospheric Research will provide science expertise to develop science domain ontologies and guide design of the prototype.
The result of this research will be a prototype semantic knowledge integration framework, SKIF, comprising a toolkit of data mining and knowledge extraction web services designed specifically for NASA data, and a series of linked ontologies describing both the data mining, manipulation and analysis services as well as the science problem domain. A web-based user interface will use the ontologies to allow users to discover and explore available data and services, compose workflows of data access, data mining and related services appropriate for their tasks, and invoke them to perform the desired analysis.
This proof of concept will serve two important purposes. First, SKIF will be a useful tool to assist researchers in creating data analysis workflows to address targeted Earth-Sun System science problems. Perhaps more importantly, associating semantic information with these knowledge extraction, manipulation and analysis services will not only address immediate usability concerns, it will also position them for integration with many other science data services in the emerging Semantic Web Services context. |
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