Using CiteSeer to analyze trends in the ACM's computing classification system

Abstract

Classification systems for research publications are often based on taxonomies. The ACM society for computing and professionals provides a digital library whose cataloguing system is based on a taxonomy that has been continuously updated over the years. The CiteSeer collection contains a large collection of computer science research papers, many of which are tagged with categories from the ACM's taxonomy. By analyzing the small portion of CiteSeer's manually tagged documents and by considering different time frames, we extracted statistics that shows how the ACM's taxonomy covers the publications in computer and information science research sub-fields. We also studied size and growth of categories over the last four available years. These results allow us to reveal areas with higher or lower publication rate. We believe that these techniques could be exploited to quickly identify trends within taxonomies. This would greatly help to construct more efficient browsing and searching systems.