Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings
TABLE I: Comparison of performance of models
['[EMPTY]', 'Precision', 'Recall', 'F1']
In testing our ensemble model, we used another instance corpus. In this corpus, we subdivided in the order of 70%, 20%, and 10% as the training set, the validation set, and the test set respectively. Training set was used in training the models individually. Validation set was used to fine tune the models. Finally, the test set was used in verifying the accuracy of the models. In Fig. Hence, from the results obtained, as a proof of concept, we can demonstrate that word embeddings can be used effectively in semi-supervised ontology population.