Representation of Document Archives for Interactive Exploration

Today's information age may be characterized by constant massive production and dissemination of written information. More powerful tools for exploring, searching, and organizing the available mass of information are needed to cope with this situation. In this context the map metaphor for displaying the contents of a document archive in a two-dimensional display has gained increased interest. In particular, we rely on self-organizing maps, which produce a map of the document space after their training process. From geography, however, it is known that maps are not always the best way to represent information spaces. For most applications it is better to provide a hierarchical view of the underlying data collection in form of an atlas, where, starting from a map representing the complete data collection, di erent regions are shown at ner levels of granularity. Using an atlas, the user can easily \zoom" into regions of particular interest while still having general maps for overall orientation. We show that a similar display can be obtained by using the Growing Hierarchical Self-Organizing Map (GHSOM) to represent the contents of a document archive. This neural network model has an adaptive layered architecture where each layer consists of a number of individual self-organizing maps. By this, the contents of the text archive may be represented at arbitrary detail while still having the general maps available for global orientation.