Representing scientific knowledge using ontologies enables data integration, consistent machine-readable data representation, and allows for large-scale computational analyses. Text mining approaches that can automatically process and annotate scientific literature with ontology concepts are necessary to keep up with the rapid pace of scientific publishing. Here, we present deep learning models (Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) combined with different input encoding formats for automated Named Entity Recognition (NER) of ontology concepts from text. The Colorado Richly Annotated Full Text (CRAFT) gold standard corpus was used to train and test our models. Precision, Recall, F-1, and Jaccard semantic similarity were used to evaluate the performance of the models. We found that GRU-based models outperform LSTM models across all evaluation metrics. Surprisingly, considering the top two probabilistic predictions of the model for each instance instead of the top one resulted in a substantial increase in accuracy. Inclusion of ontology semantics via subsumption reasoning yielded modest performance improvement.