Automatic Taxonomy Extraction from Bipartite Graphs

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, 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. Our proposed method has the following desirable properties: (a) it requires no user-defined parameters, by exploiting the principle of minimum description length, (b) it is effective, by utilizing the inheritance of objects when representing the hierarchy, and (c) it is scalable, being linear in the number of edges. We demonstrate the effectiveness and scalability of our method on a broad spectrum of real, publicly available graphs from drug-property graphs to social networks with up to 22 million vertices and 286 million edges.