SemMedDB: a PubMed-scale repository of biomedical semantic predications

By H. Kilicoglu, Dongwook Shin, Marcelo Fiszman, Graciela Rosemblat, Thomas C. Rindflesch. Bioinform., 2012

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ProjE: Embedding Projection for Knowledge Graph Completion (2016, methodology): “The FB15K dataset is a 15,000-entity subset of Freebase; the Semantic MEDLINE Database (SemMedDB) is a KG extracted from all of PubMed [20]; and DBpedia is KG extracted from Wikipedia infoboxes [23].”
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery (2015, background): “To achieve this, the graph builder collects the semantic predications for each document in D that are also present in SemMedDB.”
Open-World Knowledge Graph Completion (2017, background): “Indeed, most KGs such as FreeBase (Bollacker et al. 2008), DBPedia (Lehmann et al. 2015), and SemMedDB (Kilicoglu et al. 2012) were either directly extracted from (Lin et al. 2016; Ji and Grishman 2011), or are built in parallel to some underlying textual descriptions.”
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations (2018, methodology): “2 SVM with SemMed Database In the last set of experiments, we study the performances of SODDA with the (b, c, d) selected in the previous section and RADiSA-avg on the Semantic MEDLINE Database [9] with SemRep, a semantic interpreter of biomedical text [16] as an extraction tool to construct the kno”