Binarized Embeddings for Fast, Space-Efficient Knowledge Graph Completion

Abstract

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion. However, existing embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large. We present a binarized CANDECOMP/PARAFAC (CP) decomposition algorithm, which we refer to as B-CP, where real-valued parameters are replaced by binary values to reduce model size. Moreover, a fast score computation technique is developed with bitwise operations. We prove that B-CP is fully expressive given a sufficiently large dimensionality of embedding vectors. Experimental results on several benchmark datasets demonstrate that the proposed method successfully reduces model size by more than an order of magnitude while maintaining task performance at the same level as the real-valued CP model.