  Here we study the semantic search and retrieval problem in biomedical digital
libraries. First, we introduce MedGraph, a knowledge graph embedding-based
method that provides semantic relevance retrieval and ranking for the
biomedical literature indexed in PubMed. Second, we evaluate our method using
PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a
traditional TFIDF based algorithm. We use a dataset extracted from PubMed,
including 30 million articles' metadata such as abstracts, author information,
citation information, and extracted biological entity mentions. We do that by
pulling a subset of the dataset to evaluate MedGraph using predefined queries
with ground truth ranked results. To our knowledge, this technique has not been
explored before in biomedical information retrieval. In addition, our results
provide evidence that semantic approaches to search and relevance in biomedical
digital libraries that rely on knowledge graph modeling offer better search
relevance results when compared with traditional approaches in terms of
objective metrics.
