Building Semantic Knowledge of Large Data Sets through Collaborative Visual Approaches

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911QX-12-C-0098
Agency Tracking Number: O113-DR4-2128
Amount: $149,044.00
Phase: Phase I
Program: SBIR
Awards Year: 2012
Solitcitation Year: 2011
Solitcitation Topic Code: OSD11-DR4
Solitcitation Number: 2011.3
Small Business Information
Intelligent Models, Inc.
9710 Traville Gateway Drive, Rockville, MD, -
Duns: 626985399
Hubzone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Yuri Levchuk
 Chief Scientist
 (240) 401-9746
 yuri_levchyk@intelligentmodels.com
Business Contact
 Farida Badalova
Title: COO
Phone: (571) 236-5150
Email: farida_badalova@intelligentmodels.c
Research Institution
 Stub
Abstract
Human Social and Cultural and Behavior (HSCB) models are increasingly used to provide critical support to US military decision making. HSCB models are highly reliant on data; they need data from many sources, types, and areas of human behavior. The concomitant data streams have variable data quality and are constantly changing. Despite these challenges, HSCB applications may need near real-time access to relevant knowledge to make rapid decisions. To promote rapid acquisition and effective use of relevant HSCB knowledge, we propose SELMA (Semantic Exploration of Large Multi-modal Archives) which automates the processes of: (1) semantic cross-modal exploration of HSCB archives (to link people, events, objects, knowledge, and actions); (2) data-mining to fill gaps in information, resolve uncertainty, and classify behaviors and events; (3) mission-critical HSCB knowledge discovery; and (4) binding and visualizing the captured HSCB insights and semantic knowledge. Together, these components allow SELMA to offer a solution to HSCB data needs that: (1) collects, stores, and analyses mission-critical HSCB insights, (2) works autonomously for extended periods of time, and (3) actively reasons over the regional Human Terrains. SELMA dynamically creates an HSCB knowledge meta-network, explores concepts, discovers relationships with certain properties, and carries out versatile on-demand analyses.

* information listed above is at the time of submission.

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