Knowledge Enhanced Graph Neural Networks for Graph Completion
This work addresses graph completion for applications in natural science, social networks, and the semantic web, but it is incremental as it builds on existing graph neural network methods.
The authors tackled the problem of incomplete and noisy graph data by proposing Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework that integrates prior knowledge into graph neural networks for graph completion tasks like node classification, achieving improved performance on benchmark datasets.
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.