AIDEN: Analysis of Intelligence Data for Evidence of Networks

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$69,975.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
N00014-10-M-0455
Award Id:
n/a
Agency Tracking Number:
N102-180-0167
Solicitation Year:
2010
Solicitation Topic Code:
N102-180
Solicitation Number:
2010.2
Small Business Information
6011 West Courtyard Drive, Bldg 5, Suite 300, Austin, TX, -
Hubzone Owned:
N
Minority Owned:
Y
Woman Owned:
Y
Duns:
158034665
Principal Investigator:
Matthew McClain
Principal Investigator
(512) 682-4735
mmcclain@21technologies.com
Business Contact:
Irene Williams
CEO
(512) 682-4700
SBIR_Admin@21technologies.com
Research Institution:
Stub




Abstract
The goal of intelligence, surveillance, and reconnaissance (ISR) operations is to provide analysts and warfighters with situational awareness information. ISR enterprise systems store data to support these operations, but large volumes make it difficult to locate information of interest, such as key individuals. Current keyword-search approaches fall short because they fail to capture semantics. Social network analysis (SNA) techniques, including group detection and SNA metric-based classification, have been demonstrated to find valuable information in graphs, such as networks of interest and the roles of individuals in those networks. Current natural language processing (NLP) tools extract entities, themes, and relationships from unstructured text. By combining such NLP tools, information in text-based documents can be converted into a graph. The application of SNA techniques to such graphs has the potential to enable analysts and warfighters to efficiently locate actionable intelligence in ISR enterprise datastores. 21st Century Technologies (21CT) proposes Analysis of Unstructured Data for Evidence of Networks (AIDEN), a tool for locating information of interest in large, diverse data sources. Our Phase 1 work will focus on a proof-of-concept for applying SNA on graphs obtained from NLP, and identification of gaps in state-of-the-art NLP that will need to be overcome.

* information listed above is at the time of submission.

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