I am new to machine learning and want to classify sentences using ontologies (taxonomies/ knowledge graphs) and supervised learning methods (I have an annotated training dataset).
My question is how to use the ontology for this task? Is the following method correct?
I will first perform the tokenization, stemming and stop word removal (pre-processing). Then, I will search for each term in the ontology and after finding them, I will add their related hierarchy to an array or vector for each document. Then I will train the classifiers (supervised) on those vectors.
Please let me know if this method is correct or if there are steps that I am missing here.