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.

翻译：图数据无处不在，在自然科学、社交网络或语义网等领域有着广泛的应用。然而，尽管图数据信息丰富，但它们常常存在噪声且不完整。因此，节点分类或链接预测等图补全任务受到了关注。一方面，图神经网络等神经方法已被证明是学习带噪图数据丰富表示的稳健工具。另一方面，符号方法能够对图进行精确推理。我们提出了知识增强图神经网络（KeGNN），这是一种用于图补全的神经符号框架，它结合了两种范式，允许将先验知识集成到图神经网络模型中。本质上，KeGNN以图神经网络为基础，在其上叠加知识增强层，旨在根据先验知识细化预测。我们结合两种最先进的图神经网络——图卷积网络和图注意力网络——实例化了KeGNN，并在多个节点分类基准数据集上对其进行了评估。