Extracting Taxonomies from Bipartite Graphs

Tobias Kötter, Stephan Günnemann, Christos Faloutsos, Michael R. Berthold

Abstract: Given a large bipartite graph that represents objects and their properties, how can we automatically extract semantic information that provides an overview of the data and -- at the same time -- enables us to drill down to specific parts for an in-depth analysis? In this work in-progress paper, we propose extracting a taxonomy that models the relation between the properties via an is-a hierarchy. The extracted taxonomy arranges the properties from general to specific providing different levels of abstraction.