Before revision:  We propose ConTraKG , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context. A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
After revision:  We propose ConTraKG , a neuro-symbolic approach that supervises the training using image-data-invariant auxiliary knowledge. The auxiliary knowledge is first encoded in a knowledge graph with respective concepts and their relationships , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.