  This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method,
which projects KG entities and relations into the Hamming space based on a
computationally tractable discrete optimization algorithm, to solve the
formidable storage and computation cost challenges in traditional continuous
graph embedding methods. The convergence of DKGE can be guaranteed
theoretically. Extensive experiments demonstrate that DKGE achieves superior
accuracy than classical hashing functions that map the effective continuous
embeddings into discrete codes. Besides, DKGE reaches comparable accuracy with
much lower computational complexity and storage compared to many continuous
graph embedding methods.
