Challenges exist for intelligence analysts to efficiently and accurately process large amounts of data collected from a
myriad of available data sources. These challenges are even more evident for analysts who must operate within small
military units at the tactical edge. In such environments, decisions must be made quickly without guaranteed access to
the kinds of large-scale data sources available to analysts working at intelligence agencies. Improved technologies must
be provided to analysts at the tactical edge to make informed, reliable decisions, since this is often a critical collection
point for important intelligence data. To aid tactical edge users, new types of intelligent, automated technology
interfaces are required to allow them to rapidly explore information associated with the intersection of hard and soft data
fusion, such as multi-INT signals, semantic models, social network data, and natural language processing of text.
Abilities to fuse these types of data is paramount to providing decision superiority. For these types of applications, we
have developed BLADE. BLADE allows users to dynamically add, delete and link data via a semantic wiki, allowing
for improved interaction between different users. Analysts can see information updates in near-real-time due to a
common underlying set of semantic models operating within a triple store that allows for updates on related data points
from independent users tracking different items (persons, events, locations, organizations, etc.). The wiki can capture
pictures, videos and related information. New information added directly to pages is automatically updated in the triple
store and its provenance and pedigree is tracked over time, making that data more trustworthy and easily integrated with
other users’ pages.