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.

中文翻译：

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基于离散优化的离散知识图嵌入

本文提出了一种离散知识图（KGGE）嵌入（DKGE）方法，该方法基于可计算的离散优化算法将KG实体和关系投影到汉明空间中，以解决传统连续图嵌入方法中的巨大存储和计算成本挑战。从理论上可以保证DKGE的收敛性。大量实验表明，与将有效连续嵌入映射为离散代码的经典哈希函数相比，DKGE的准确性更高。此外，与许多连续图形嵌入方法相比，DKGE以相当低的计算复杂性和存储量达到了可比的精度。