Massively Scalable Themes, Entities and Relationships (MASTER)

Discovering the important evidence in the form of activities, actors and relationships in a sea of open source data requires the ability to extract and correlate seemingly unrelated pieces of data, distinguish that data from the noise of harmless civilian activity and find the hidden attributes and relationships that characterize the true threat. To meet these requirements, the DAC BOBCAT Team proposes to extend a new suite of algorithms developed that enable current NLP applications to be immediately available across all levels of military intelligence. We call this approach the Massively Scalable Themes, Entities, and Relationships (MASTER). In the MASTER approach, we overcome the scalability limitations of current NLP approaches while also enabling the tactical warfighter to focus queries based on discovered context and relations. The development of the MASTER approach for the tactical warfighter will result in a suite of algorithms that will support all levels of the fight. The current suite of BOBCAT algorithms, which already advance the state of the art in statistical theme discovery, executes on enterprise platforms at the strategic and operational levels. The MASTER algorithms will specifically focus on the tactical levels with limited computing footprints and large amounts of open-source data.