Learning to Hash Knowledge Graph: Element-wise Rotation

Knowledge graphs are vital for many tasks, including recommendation systems and node search. Learning to hash knowledge graph is to infer binary-vector representations of the graph. Compared with traditional knowledge graph embedding that learns continuous-vector representations, knowledge graph hashing could significantly reduce storage and computational time due to its binary nature. Despite the potential advantage, the problem of knowledge graph hashing is challenging due to the large-scale binary decision variables. In this article, we propose a novel discrete optimization framework for knowledge graph hashing. We treat the relations between heads and tails in the knowledge graph as element-wise rotation to learn binary codes. An alternating optimization algorithm is then proposed to produce high-quality code that captures knowledge graph information well. Furthermore, to obtain superior binary representations, we employ a dynamic range method during the alternating optimization process to adjust the approximations of the ReLU function \([x]_{+}\) . This ensures that valuable measures of dissimilarity are not overlooked, leading to more accurate computations. The evaluation results on five publicly available datasets demonstrate the superiority of the proposed algorithm against several state-of-the-art baseline methods.