Before revision:  However, their accuracy suffers considerably when they face a domain change, i. e., as soon as they are used in a domain that differs from their training domain. For example, a road sign recognition model trained to recognize road signs in Germany performs poorly in countries with different road sign standards like China. We propose ConTraKG , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context.
After revision:  However, their accuracy suffers considerably when they face a domain change, i. e., as soon as they are used in a domain that differs from their source domain. To learn a more robust NN to domain shifts, we propose the knowledge graph neural network (KG-NN) , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context.