PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning. (arXiv:2204.13957v1 [cs.CL])

Knowledge graph (KG) embedding methods which map entities and relations to
unique embeddings in the KG have shown promising results on many reasoning
tasks. However, the same embedding dimension for both dense entities and sparse
entities will cause either over parameterization (sparse entities) or under
fitting (dense entities). Normally, a large dimension is set to get better
performance. Meanwhile, the inference time grows log-linearly with the number
of entities for all entities are traversed and compared. Both the parameter and …

arxiv embedding graph inference knowledge knowledge graph reasoning scale solution