  In many modern day systems such as information extraction and knowledge
management agents, ontologies play a vital role in maintaining the concept
hierarchies of the selected domain. However, ontology population has become a
problematic process due to its nature of heavy coupling with manual human
intervention. With the use of word embeddings in the field of natural language
processing, it became a popular topic due to its ability to cope up with
semantic sensitivity. Hence, in this study, we propose a novel way of
semi-supervised ontology population through word embeddings as the basis. We
built several models including traditional benchmark models and new types of
models which are based on word embeddings. Finally, we ensemble them together
to come up with a synergistic model with better accuracy. We demonstrate that
our ensemble model can outperform the individual models.
