Discrete Knowledge Graph Embedding based on Discrete Optimization
This addresses efficiency challenges in knowledge graph applications, though it is incremental as it builds on existing embedding techniques.
The paper tackles the high storage and computation costs of continuous knowledge graph embeddings by proposing a discrete embedding method based on discrete optimization, achieving comparable accuracy with lower complexity and storage.
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.