  Methods based on vector embeddings of knowledge graphs have been actively
pursued as a promising approach to knowledge graph completion.However,
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, we show that a fast score computation technique can be developed with
bitwise operations. We prove that B-CP is fully expressive by deriving a bound
on the size of its embeddings. 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.
