TWC Collaborator

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William Smith
Contact Info
Emailwilliam.smith@pnnl.gov

William Smith is a Scientist / Engineer III at Pacific Northwest National Laboratory.

Publications:
Presentations:
Purohit, S., Smith, W., Chappell, A., West, P., Lee, B., Stephan, E., & Fox, P. (February 4, 2016). Effective Tooling for Linked Data Publishing in Scientific Research. In: IEEE Tenth International Conference on Semantic Computing (ICSC) (2016)
Lee, B., Purohit, S., Smith, W., Weaver, J., Chappell, A., West, P., & Fox, P. (December 17, 2014). Publishing and Visualizing Large-Scale Semantically-enabled Earth Science Resources on the Web. In: AGU Fall Meeting 2014
Lee, B., West, P., Smith, W., Purohit, S., Schuchardt, K., Chappell, A., Fox, P., & Weaver, J. (September 16, 2014). Resource Discover for Extreme Scale Collaboration - ASCR Poster 2014.
Weaver, J., Chappell, A., Smith, W., Purohit, S., Fox, P., West, P., & Lee, B. (September 15, 2014). Resource Discovery for Extreme Scale Collaboration.

Project PI

TW LogoStreaming Hypothesis Reasoning (Shyre)
Principal Investigator: William Smith and Deborah L. McGuinness
Description: AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms.

Project Collaborator

Resource Discovery for Extreme Scale Collaboration (RDESC)
Principal Investigator: Karen Schuchardt, Jesse Weaver, and Eric Stephan
Co Investigator: Alan Chappell and Peter Fox
Description: Our objective is to develop a capability for describing, linking, searching and discovering resources used in collaborative science that is lightweight enough to be used as a component in any software system such as desktop user environments or dashboards but also scalable to millions of resources. A key design goal is to offer local control over resource descriptions thus reducing one of the bottlenecks to widespread adoption. We propose to build a prototype framework and associated services, the Resource Discovery for Extreme Scale Collaboration (RDESC), that meet these objectives.
SDC LogoStreaming Data Characterization (SDC)
Co Investigator: Deborah L. McGuinness
Description: This project aims to leverage the novel notion of semantic importance to characterize the importance among the boundless streaming data, so as to provide better query results in terms of accuracy or recall, as well as improve the system response time.