  Finding related published articles is an important task in any science, but
with the explosion of new work in the biomedical domain it has become
especially challenging. Most existing methodologies use text similarity metrics
to identify whether two articles are related or not. However biomedical
knowledge discovery is hypothesis-driven. The most related articles may not be
ones with the highest text similarities. In this study, we first develop an
innovative crowd-sourcing approach to build an expert-annotated
document-ranking corpus. Using this corpus as the gold standard, we then
evaluate the approaches of using text similarity to rank the relatedness of
articles. Finally, we develop and evaluate a new supervised model to
automatically rank related scientific articles. Our results show that authors'
ranking differ significantly from rankings by text-similarity-based models. By
training a learning-to-rank model on a subset of the annotated corpus, we found
the best supervised learning-to-rank model (SVM-Rank) significantly surpassed
state-of-the-art baseline systems.
