Despite the vast amount of information encoded in knowledge graphs, they often remain incomplete. Neural networks, in particular Graph Convolutional Neural Networks, have been shown to be effective predictors to complete information about the class affiliation of entities in knowledge graphs. However, these models remain ignorant to their predictions confidence due to their used point estimate of a softmax output. In this paper, we combine Graph Convolutional Neural Networks with recent developments in the field of Evidential Learning by placing a Dirichlet distribution on the class probabilities to overcome this problem. We use the continuous output of a Graph Convolutional Neural Network as parameters for a Dirichlet distribution. In this way, the predictions of the model are represented as a distribution over possible softmax outputs, rather than a point estimate of a softmax output. The experiments show that a better performance in predicting class affiliations can be achieved compared to recent models. In addition, the experiments show that this approach overcomes the well-known problem of overconfident prediction of deterministic neural networks.
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