This thesis is about methods to tackle large quantities of information. It will give an introduction to how information is structured to increase understanding and how to present this in a simple and effective manner. The emphasis is on how to reduce all of the information which is browsable in an application to only that which is relevant for a reader.
We will give an introduction to a technology known as topic maps, which is a tool for organizing and navigating large information pools. They are very powerful when it comes to organizing information, but they tend to become very large. This presents a problem because it makes the navigation of the topic map more difficult.
The information inherent in a topic map, which can be characterized as a semantic network, can be visualized as a graph. This graph can be used to simplify the topic map itself. This will be done on the basis of the ab modeling language, a simple language that represents information models as directed graphs and which includes a set of operations to reduce a graph in order to reach simpler information representations. Because of the ab modeling language s behaviour perspective, the emphasis is on causal models of information, i.e how a change in one concept induces a change in another concept. Most of the methods themselves are based on these behaviour oriented models.