MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed.

ABSTRACT: 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 approach using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TF-IDF-based algorithm. Third, 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 pull 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 inform

SUBMITTER: Ebeid IA 

PROVIDER: S-EPMC9627348 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature