This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves
performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these
triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the
highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data
set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the
process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and
potentially useful results.