Comparison of Generality Based Algorithm Variants for Automatic Taxonomy Generation
Name
SSRN-id1478201.pdf
Size
707 KB
Format
Adobe PDF
Checksum (MD5)
6b6a7fa5c466c8f70531d1db65e27b9b
Author(s)
Henschel, Andreas
Woon, Wei Lee
Wachter, Thomas
Madnick, Stuart
Date Issued
September 2009
Publisher
Cambridge, MA; Alfred P. Sloan School of Management, Massachusetts Institute of Technology
Series/Report no.
MIT Sloan School of Management Working Paper;4758-09
CISL Working Paper;2009-12
Abstract
We compare a family of algorithms for the automatic generation of taxonomies by adapting the Heymannalgorithm 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.
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