Semantical Machine Understanding

Defense Transformation has changed warfighting tactics, requiring quick reaction, team-based mobile force operations in discrete events. Joint, Coalition, Non-Government and Volunteer Organizations need to analyze open-source, uncertain, conflicting, partial, non-official data. More powerful information analysis tools are needed that can quickly extract meaning, intent and specified semantic content from large volumes of unstructured multilingual text and represent it mathematically. We propose to develop an innovative semantical machine learning, understanding and search architecture to meet the needs of Semantical Machine Understanding. Our innovation is to integrate an agent social network and text mining within a reinforcement learning framework, therefore, it is cognitive, human-in-loop and real-time. It is an infrastructure allowing incorporating human interactions in the loop to gradually enhance machine understanding. Real-time and collaborative machine learning with initial innovative text mining is to provide the ultimately practical approach for semantical machine understanding and search. If successful, this paradigm would be much powerful than a static text analysis tool and would be readily applied as the search technology in a planned operational test environment at a Navy or Defense Intelligence Analysis venue and identify events, relationships and trends to enable interoperable knowledge sharing and intelligence analysis across joint and coalition forces.