/fkg-mini-project

Machine Learning (ML) algorithms need data in the form of feature vectors and class labels to come up with a trained model and predict new instances with that trained model. Data stored in Knowledge Graph(s) (KG) is in the form of triples. To apply ML algorithms on KG, we need to convert the individuals in the learning problem into feature vectors. One way to tackle this task is to use KG embedding.

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