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

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.