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.

翻译：本文提出一种离散知识嵌入图(KG)嵌入法,该法根据可计算可移动的离散优化算法预测KG实体和与Hamming空间的关系,以解决传统连续图形嵌入法中巨大的存储和计算成本挑战。DKGE在理论上可以保证汇合。广泛的实验表明,DKGE比将有效连续嵌入离散代码的古典散列函数具有更高的准确性。此外,DKGE的精确性与许多连续的图形嵌入法相比要低得多,其计算复杂性和存储率也低得多。