Information DiscovEry Assistant that Learns (IDEAL)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-M-0054
Agency Tracking Number: N112-152-1304
Amount: $149,997.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: N112-152
Solicitation Number: 2011.2
Small Business Information
1408 University Drive East, College Station, TX, -
DUNS: 555403328
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Perakath Benjamin
 Senior Research Scicentis
 (979) 260-5274
 pbenjamin@kbsi.com
Business Contact
 Donielle Mayer
Title: Business Operations Manag
Phone: (979) 260-5274
Email: dmayer@kbsi.com
Research Institution
 Stub
Abstract
Traditional keyword search fails to adequately meet the needs of the modern intelligence analyst. If analysts were able to express their information needs in plain terms, understood by a search engine as guidance or examples, documents and other information artifacts might be brought to light that simply"guessing at"appropriate keywords would never elicit. Further, because analysts seldom work in isolation, a shared understanding of analysis goals and subsequent sharing of knowledge and effort can significantly improve analytic outcomes. KBSI proposes to design, configure, and demonstrate an innovative solution called the Information DiscovEry Assistant that Learns (IDEAL) 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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