Research article

PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

Jimmy Lin^1,2

¹National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, USA

²The iSchool, University of Maryland, College Park, Maryland, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:270doi:10.1186/1471-2105-9-270

Published:	6 June 2008

Abstract

Background

Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed^® search interface, a MEDLINE^® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web.

Results

We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics.

Conclusion

The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain.