  Graph data is omnipresent and has a wide variety of applications, such as in
natural science, social networks, or the semantic web. However, while being
rich in information, graphs are often noisy and incomplete. As a result, graph
completion tasks, such as node classification or link prediction, have gained
attention. On one hand, neural methods, such as graph neural networks, have
proven to be robust tools for learning rich representations of noisy graphs. On
the other hand, symbolic methods enable exact reasoning on graphs.We propose
Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework
for graph completion that combines both paradigms as it allows for the
integration of prior knowledge into a graph neural network model.Essentially,
KeGNN consists of a graph neural network as a base upon which knowledge
enhancement layers are stacked with the goal of refining predictions with
respect to prior knowledge.We instantiate KeGNN in conjunction with two
state-of-the-art graph neural networks, Graph Convolutional Networks and Graph
Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node
classification.
