Information DiscovEry Assistant that Learns (IDEAL)

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$482,560.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
N00014-12-C-0600
Award Id:
n/a
Agency Tracking Number:
N112-152-1304
Solicitation Year:
2011
Solicitation Topic Code:
N112-152
Solicitation Number:
2011.2
Small Business Information
1408 University Drive East, College Station, TX, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
555403328
Principal Investigator:
Perakath Benjamin
Senior Research Scientist
(979) 260-5274
pbenjamin@kbsi.com
Business Contact:
Donielle Mayer
Business Operations Manag
(979) 260-5274
dmayer@kbsi.com
Research Institute:
Stub




Abstract
Knowledge Based Systems, Inc. (KBSI) proposes to establish innovative enabling methods that target the challenge of semantic information analysis to facilitate iterative and adaptive discovery of information content associated with imprecisely specified information needs. The resulting new technology is expected to empower analysts working on complex intelligence tasks by dynamically forming collaborative research groups of individuals who are currently investigating similar issues, while requiring minimal or no direct input from each analyst with respect to the goals of their current mission. KBSI proposes to design, build, and validate an Information DiscovEry Assistant that Learns (IDEAL) solution that addresses the challenges described above. IDEAL will be designed to provide the following advanced capabilities (1) perform semantic search across multiple data sources to discover data that is relevant to an analyst"s goals and tasks; (2) progressively refine the quality of information produced through iterative semantic search and knowledge discovery; (3) facilitate dynamic information sharing and collaboration by a group of human agents; and (4) provide learning and adaptation by using the results and analyst feedback from the previous search/analysis activities to incrementally refine the semantic mapping and search models over extended time.

* information listed above is at the time of submission.

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