This paper presents a new information model to help intelligence analysts in organizing, querying, and visualizing the information present in large volumes of unstructured data sources such as text reports, multi-media, and human discourse. Our primary goal is to create a system that would combine the human pattern recognition abilities of intelligence analysis with the storage and processing capabilities of computers. Our system models the collective mental map of intelligence analysts in the form of the <i>Correlation Graph</i>, a modified graph data structure with objects and events as nodes and subjective probabilistic correlations between
them as edges. Objects are entities such as people, places, and things. Events are actions that involve the objects. A taxonomy is also associated with the model to enable intelligence domain specific querying of the data. Graph drawing techniques are used to visualize the information represented by the correlation graph. Through real world examples, we demonstrate that the resulting information model can be used for efficient representation, presentation, and querying to discover novel patterns in the intelligence data via graph visualization techniques.