A comparison of taxonomy generation techniques using bibliometric methods : applied to research strategy formulation

Abstract

This paper investigates the modeling of research landscapes through the automatic generation of hierarchical structures (taxonomies) comprised of terms related to a given research field. Several different taxonomy generation algorithms are discussed and analyzed within this paper, each based on the analysis of a data set of bibliometric information obtained from a credible online publication database. Taxonomy generation algorithms considered include the Dijsktra-Jamik-Prim's (DJP) algorithm, Kruskal's algorithm, Edmond's algorithm, Heymann algorithm, and the Genetic algorithm. Evaluative experiments are run that attempt to determine which taxonomy generation algorithm would most likely output a taxonomy that is a valid representation of the underlying research landscape.