Generalized Ontology Discovery Enabling Semantic Search (GODESS)

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$100,000.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
N00014-10-M-0453
Agency Tracking Number:
N102-180-0708
Solicitation Year:
2010
Solicitation Topic Code:
N102-180
Solicitation Number:
2010.2
Small Business Information
Aptima, Inc.
12 Gill Street, Suite 1400, Woburn, MA, -
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
967259946
Principal Investigator:
Charlotte Shabarekh
Modeling and Simulation S
(781) 496-2465
cshabarekh@aptima.com
Business Contact:
Thomas McKenna
Chief Financial Officer
(781) 496-2443
mckenna@aptima.com
Research Institution:
Stub




Abstract
A central challenge in the intelligence community is managing and effectively integrating large amounts of disparate information sources for concise presentation of knowledge to an analyst. Currently, the high volume of near-constant incoming intelligence imposes a substantial burden on the analyst to review and digest the raw intelligence, causing analyst overload and fatigue that could lead to missed intelligence. Generalized Ontology Discovery Enabling Semantic Search (GODESS) addresses this urgent need by analyzing the data"s structure and the analyst"s information needs to provide customized, data- and goal-driven knowledge extracted from disparate, multi-modal document stores. GODESS automatically discovers systems of related concepts structured in the data to learn ontologies that are optimal for representing the knowledge encapsulated in the database relearning them as new data sources become available, making the approach tractable for environments with continuous, high-volume information flow. In addition to automatically optimizing semantic search based on the data, GODESS optimizes on the user, returning a dynamic semantic network that targets the user"s specific search needs, conditioned on their queries and information objectives. The returned network is a granular graph representation of relevant knowledge, fused at the datastore level, capturing semantic distance between entities providing concise, relevant results across disparate datastores.

* information listed above is at the time of submission.

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