OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services

Authors:
John Cheng;Ray Emami;Larry Kerschberg;Eugene Santos, Jr,.;Qunhua Zhao;Hien Nguyen;Hua Wang;Michael Huhns;Marco Valtorta;Jiangbo Dang;Hrishikesh Goradia;Jingshan Huang;Sharon Xi
Affiliations:
Global InfoTek;Global InfoTek;KRM, Inc;University of Connecticut;University of Connecticut;University of Connecticut;University of Connecticut;University of South Carolina;University of South Carolina;University of South Carolina;University of South Carolina;University of South Carolina;University of South Carolina
Venue:
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Year:
2005

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Abstract

This paper describes the current state of the OmniSeer system. OmniSeer supports intelligence analysts in the handling of massive amounts of data, the construction of scenarios, and the management of hypotheses. OmniSeer models analysts with dynamic user models that capture an analyst's context, interests, and preferences, thus enabling more efficient and effective information retrieval. OmniSeer explicitly represents the prior and tacit knowledge of analysts, thus enabling transfer and reuse of such knowledge. Both the user and cognitive models employ a Bayesian network fragment representation, which supports principled probabilistic reasoning and analysis. An independent evaluation of OmniSeer was carried out at NIST and will be used to guide further development.