Abstract: A method may compiling, using at least one processor, a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object; generating a vocabulary of terms based on the corpus of training data; training a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms; and storing a set of word embeddings for the vocabulary of terms based on the trained semantic model.