Graph data is omnipresent and has a large variety of applications such as natural science, social networks or semantic web. Though rich in information, graphs are often noisy and incomplete. Therefore, graph completion tasks such as node classification or link prediction have gained attention. On the 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 KeGNN, a neuro-symbolic framework for learning on graph data that combines both paradigms and allows for the integration of prior knowledge into a graph neural network model. In essence, KeGNN consists of a graph neural network as a base on which knowledge enhancement layers are stacked with the objective of refining predictions with respect to prior knowledge. We instantiate KeGNN in conjunction with two standard 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、社交网络或语义网络等领域有着广泛的应用。尽管图数据包含丰富的信息，但它们通常充满噪声且不完整。因此，节点分类或链接预测等图补全任务引起了广泛关注。一方面，图神经网络等神经方法已被证明是学习含噪图数据丰富表示的鲁棒工具。另一方面，符号方法能够对图数据进行精确推理。我们提出KeGNN，这是一种用于图数据学习的神经符号框架，它结合了两种范式，并允许将先验知识集成到图神经网络模型中。本质上，KeGNN以一个图神经网络为基础，在其上堆叠知识增强层，目的是根据先验知识优化预测结果。我们将KeGNN与两种标准图神经网络（图卷积网络和图注意力网络）结合实例化，并在多个节点分类基准数据集上评估KeGNN的性能。