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Bioinformatics Advance Access originally published online on June 3, 2009
Bioinformatics 2009 25(15):1944-1951; doi:10.1093/bioinformatics/btp338
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity

Shanfeng Zhu 1,2,*, Jia Zeng 3 and Hiroshi Mamitsuka 4

1 Shanghai Key Lab of Intelligent Information Processing, Fudan University, 2 School of Computer Science, Fudan University, Shanghai 200433, China, 3 Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong and 4 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan

* To whom correspondence should be addressed.


  Abstract

Motivation: Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH thesaurus have two serious limitations: first, important semantic information may be lost when generating MeSH concept vectors, and second, the content information of the original text has been discarded.

Methods: Our new strategy includes three key points. First, we develop a sound method for measuring the semantic similarity between two documents over the MeSH thesaurus. Second, we combine both the semantic and content similarities to generate the integrated similarity matrix between documents. Third, we apply a spectral approach to clustering documents over the integrated similarity matrix.

Results: Using various 100 datasets of MEDLINE records, we conduct extensive experiments with changing alternative measures and parameters. Experimental results show that integrating the semantic and content similarities outperforms the case of using only one of the two similarities, being statistically significant. We further find the best parameter setting that is consistent over all experimental conditions conducted. We finally show a typical example of resultant clusters, confirming the effectiveness of our strategy in improving MEDLINE document clustering.

Contact: zhushanfeng{at}gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Jonathan Wren

Received on October 30, 2008; revised on May 4, 2009; accepted on May 27, 2009

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