Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. …