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Semantic Analysis Technologies for the Identification of Dual Use Research of Concern (STIR)

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA119C0037
Agency Tracking Number: T2-0339
Amount: $997,081.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: DTRA172-004
Solicitation Number: 17.2
Solicitation Year: 2017
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-07-03
Award End Date (Contract End Date): 2021-07-02
Small Business Information
1408 University Drive East
College Station, TX 77840
United States
DUNS: 555403328
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Madhav Erraguntla
 Senior Research Scientist
 (979) 260-5274
Business Contact
 Jason Ogle
Phone: (979) 260-5274
Research Institution

The goal of this project is to design and develop Semantic Analysis Technologies for the Identification of Dual Use Research of Concern (STIR). STIR processes scientific documents using semantic technologies and inference algorithms to identify potential for Dual Use Research of Concern (DURC). The focus is on 15 high consequence pathogens and toxins and 7 experimental categories identified as DURC. STIR evaluates multiple semantic processing and inference approaches to generate the optimal DURC identification based on adaptability, precision, recall, time and effort required of subject matter experts, and ease of use. The methodology is configured for each of the seven DURC experimental categories – due to variation in the information content and DURC identification requirements. Deep semantics (extracting relationships at the molecular and cellular level, identifying biochemical entities, species, hosts, and their relationships) and shallow semantics (looking for presence of identified pathogens, hosts, and DURC experimental concepts within a sentence, neighborhood of a sentence, paragraph, or a document) are explored for DURC identification and their performance is analyzed. Fusion of the results of different DURC identification models is performed to optimize the overall DURC identification.

* Information listed above is at the time of submission. *

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