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AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:225-34. eCollection 2016.

Finding Related Publications: Extending the Set of Terms Used to Assess Article Similarity.

Author information

1
Health System Department of Biomedical Informatics, UC San Diego, San Diego, CA;
2
National Library of Medicine, National Institutes of Health, Bethesda, MD.

Abstract

Recommendation of related articles is an important feature of the PubMed. The PubMed Related Citations (PRC) algorithm is the engine that enables this feature, and it leverages information on 22 million citations. We analyzed the performance of the PRC algorithm on 4584 annotated articles from the 2005 Text REtrieval Conference (TREC) Genomics Track data. Our analysis indicated that the PRC highest weighted term was not always consistent with the critical term that was most directly related to the topic of the article. We implemented term expansion and found that it was a promising and easy-to-implement approach to improve the performance of the PRC algorithm for the TREC 2005 Genomics data and for the TREC 2014 Clinical Decision Support Track data. For term expansion, we trained a Skip-gram model using the Word2Vec package. This extended PRC algorithm resulted in higher average precision for a large subset of articles. A combination of both algorithms may lead to improved performance in related article recommendations.

PMID:
27570676
PMCID:
PMC5001748

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