Using cited references to improve the retrieval of related biomedical documents

BMC Bioinformatics

Francisco M Ortuño¹  Ignacio Rojas¹  Miguel A Andrade-Navarro²  Jean-Fred Fontaine² 

[1] Computer Architecture and Computer Technology Department, University of Granada, C/ Periodista Daniel Saucedo Aranda S/N, Granada, 18071, Spain

[2] Computational Biology and Data Mining, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, Berlin, 13125, Germany

关键词: Document classification;    Query expansion;    Biomedical literature;    Full-text documents;    Citations;    Text categorization;    Information retrieval;   

Others  :  1087922
DOI  :  10.1186/1471-2105-14-113

 received in 2012-09-25, accepted in 2013-03-18,  发布年份 2013

【 摘 要 】

Background

A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references.

Results

Data on cited references and text sections in 249,108 full-text biomedical articles was extracted from the Open Access subset of the PubMed Central® database (PMC-OA). Of the five standard sections of a scientific article, the Introduction and Discussion sections contained most of the citations (mean = 10.2 and 9.9 citations, respectively). A large proportion of articles (98.4%) and their cited references (79.5%) were indexed in the PubMed® database.

Using the MedlineRanker abstract classification tool, cited references allowed accurate retrieval of the citing document in a test set of 10,000 documents and also of documents related to six biomedical topics defined by particular MeSH® terms from the entire PMC-OA (p-value<0.01).

Classification performance was sensitive to the topic and also to the text sections from which the references were selected. Classifiers trained on the baseline (i.e., only text from the query document and not from the references) were outperformed in almost all the cases. Best performance was often obtained when using all cited references, though using the references from Introduction and Discussion sections led to similarly good results. This query expansion method performed significantly better than pseudo relevance feedback in 4 out of 6 topics.

Conclusions

The retrieval of documents related to a single document can be significantly improved by using the references cited by this document (p-value<0.01). Using references from Introduction and Discussion performs almost as well as using all references, which might be useful for methods that require reduced datasets due to computational limitations. Cited references from particular sections might not be appropriate for all topics. Our method could be a better alternative to pseudo relevance feedback though it is limited by full text availability.

【 授权许可】

   
2013 Ortuño et al.; licensee BioMed Central Ltd.

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