Cognitive information processing at higher conceptual levels requires a computational approach to knowledge representation and analysis. Semantic network analysis bridges the gap between probabilistic pattern recognition techniques and symbolic representations by replacing cumbersome and computationally complex forms of logic-based semantic inference common in symbolic approaches with mathematical metrics on graph representations of labelled, directed semantic networked data. These metrics in turn support assessment of evidentiary support for the presence of patterns of interest in which entities play specified roles in complex event scenarios. The resulting system allows patterns to be specified at higher levels of conceptual abstraction while also remaining robust to conflicting and incomplete information.