AutoNet: Rapid and Automatic Social Network Creation from Disparate Data Sources

Here we propose to develop technologies which automatically generates a social network by analyzing incoming data feeds, integrating the data around the entities and (semi-)automatically generate the relations between the entities. Because the saliency of a network is critical for important intelligence products where people are on the line, we envision that the creation of a social network be done with a human-in-the-loop, where the computer does the hard analytic lifting and the human directs the kinds of relations to look for and verifies that the relations are indeed correct before sending the network on for network analysis. We propose to investigate two key technologies which directly relate to this SBIR topic: (1) automatic extraction of relations from multiple disparate information feeds, and (2) workflows for rapid definition, creation, modification and extraction of relations. Both of these are critical pieces which are needed in order to speed up the creation of social networks for network analytics in intelligence products.