Before revision:  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:  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 contrastive loss function, KG-NN learns to adapt its visual embedding space according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.