We used an MLP classifier with the entity representations as inputs to predict the probability of entity e being a member of type t in the set of possible types T .
we used an MLP with a single hidden layer and the tanh activation function, and an output layer that contains, for each possible type t ∈ T , a logistic regression classifier that predicts the probability of t: where c e ∈ R d is the vector representation of entity e, σ is the sigmoid function, and W h ∈ R h×d and W o ∈ R |T |×h are the weight matrices corresponding to the hidden layer and the output layer, respectively.