Comparison of generality based algorithm variants for automatic taxonomy generation
Name
Henschel-2009-Comparison of generality based algorithm variants for automatic taxonomy generation.pdf
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746.52 KB
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Author(s)
Madnick, Stuart E.
Henschel, Andreas
Wachter, Thomas
Woon, Wei Lee
Date Issued
April 2010
Journal
International Conference on Innovations in Information Technology, 2009. IIT '09.
Publisher
Institute of Electrical and Electronics Engineers
Citation
Henschel, A. et al. “Comparison of generality based algorithm variants for automatic taxonomy generation.” Innovations in Information Technology, 2009. IIT '09. International Conference on. 2009. 160-164. © Copyright 2010 IEEE
Version
Final published version
Abstract
We compare a family of algorithms for the automatic generation of taxonomies by adapting the Heymann-algorithm in various ways. The core algorithm determines the generality of terms and iteratively inserts them in a growing taxonomy. Variants of the algorithm are created by altering the way and the frequency, generality of terms is calculated. We analyse the performance and the complexity of the variants combined with a systematic threshold evaluation on a set of seven manually created benchmark sets. As a result, betweenness centrality calculated on unweighted similarity graphs often performs best but requires threshold fine-tuning and is computationally more expensive than closeness centrality. Finally, we show how an entropy-based filter can lead to more precise taxonomies.
Description
Supplementary Material can be found on http://
ssm-vm011.mit.edu/henschel/IIT09/.
MIT Department
Sloan School of Management
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/IIT.2009.5413365
