2022
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
ICLR 2022poster
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector. Such a shallow lookup results in a linear growth of memory consumption for storing the embedding matrix and incurs high computational costs of working with real-world KGs. Drawing…