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

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Camiña, Steven L

Advisor dc:contributor.advisor

• Stuart Madnick and Wei Lee Woon.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/62632

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/62632