Text mining approaches for automated ontology-based curation of biological and biomedical literature have largely focused on syntactic and lexical analysis along with machine learning. Recent advances in deep learning have shown increased accuracy for textual data annotation. However, the application of deep learning for ontology-based curation is a relatively new area and prior work has focused on a limited set of models.Here, we introduce a new deep learning model/architecture based on combining multiple Gated Recurrent Units (GRU) with a character+word based input. We use data from five ontologies in the CRAFT corpus as a Gold Standard to evaluate our model's performance. We also compare our model to seven models from prior work. We use four metrics -Precision, Recall, F1 score, and a semantic similarity metric (Jaccard similarity) to compare our model's output to the Gold Standard. Our model resulted in a 84% Precision, 84% Recall, 83% F1, and a 84% Jaccard similarity. Results show that our GRU-based model outperforms prior models across all five ontologies. We also observed that character+word inputs result in a higher performance across models as compared to word only inputs.These findings indicate that deep learning algorithms are a promising avenue to be explored for automated ontology-based curation of data. This study also serves as a formal comparison and guideline for building and selecting deep learning models and architectures for ontology-based curation.
II. INTRODUCTIONOntology-based data representation has been widely adopted in data intensive fields such as biology and biomedicine due to the need for large scale computationally amenable data [1]. However, the majority of ontology-based data generation relies on manual literature curation -a slow and tedious process [2]. Natural language and text mining methods have been developed as the solution for scalable ontology-based data curation [3,4].One of the most important tasks for annotating scientific literature with ontology concepts is Named Entity Recognition p manda@uncg.edu sdmohant@uncg.edu (NER). In the context of ontology-based annotation, NER can be described as recognizing ontology concepts from text [5]. Outside the scope of ontology-based annotation, NER has been applied to biomedical and biological literature for recognizing genes, proteins, diseases, etc [5].The large majority of ontology driven NER techniques rely on lexical and syntactic analysis of text in addition to machine learning for recognizing and tagging ontology concepts [3,4,6]. In recent years, deep learning has been introduced for NER of biological entities from literature [7,8,9,10,11]. However, the majority of prior work has focused on a limited set of models, particularly, the Long Short-Term Memory (LSTM) model (e.g. [7]).Here, we present a new deep learning architecture that utilizes Gated Recurring Units (GRU) while taking advantage of word and character encodings from the annotation training data to recognize ontology concepts from text. We evaluate our model in...
