Title : 
OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services
         
        
            Author : 
Cheng, James ; Emami, Roozbeh ; Kerschberg, L. ; Qunhua Zhao ; Hien Nguyen ; Hua Wang ; Huhns, Michael N. ; Valtorta, M. ; Dang, Jian ; Jingshan Huang ; Xi, Suping
         
        
            Author_Institution : 
Global InfoTek
         
        
        
        
            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.
         
        
            Keywords : 
Bayesian methods; Collaboration; Context modeling; Data analysis; Information analysis; Information retrieval; Intelligent agent; Intelligent systems; Knowledge management; NIST;
         
        
        
        
            Conference_Titel : 
System Sciences, 2005. HICSS '05. Proceedings of the 38th Annual Hawaii International Conference on
         
        
        
            Print_ISBN : 
0-7695-2268-8
         
        
        
            DOI : 
10.1109/HICSS.2005.462