Semantic relations from the biomedical literature are increasingly used in advanced information management applications. SemMedDB is a repository of semantic relations extracted from PubMed citations by SemRep natural language processing system. In this abstract, we describe our conversion of SemMedDB to a graph database representation (SemMedDB-neo4j), suitable for literature-based discovery methods.
Learning Objective: To learn which biomedical applications and how can they benefit from a graph database of semantic relations extracted from the literature.
Dimitar Hristovski (Presenter)
University of Ljubljana
Andrej Kastrin, University of Ljubljana
Halil Kilicoglu, NIH