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Here we study the semantic search and retrieval problem in biomedical digitallibraries. First, we introduce MedGraph, a knowledge graph embedding-basedmethod that provides semantic relevance retrieval and ranking for thebiomedical literature indexed in PubMed. Second, we evaluate our method usingPubMed's Best Match algorithm. Moreover, we compare our method MedGraph to atraditional 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 bypulling a subset of the dataset to evaluate MedGraph using predefined querieswith ground truth ranked results. To our knowledge, this technique has not beenexplored before in biomedical information retrieval. In addition, our resultsprovide evidence that semantic approaches to search and relevance in biomedicaldigital libraries that rely on knowledge graph modeling offer better searchrelevance results when compared with traditional approaches in terms ofobjective metrics.

MedGraph: An experimental semantic information retrieval method using knowledge graph embedding for the biomedical citations indexed in PubMed