  Embedding-based methods for knowledge base completion (KBC) learn
representations of entities and relations in a vector space, along with the
scoring function to estimate the likelihood of relations between entities. The
learnable class of scoring functions is designed to be expressive enough to
cover a variety of real-world relations, but this expressive comes at the cost
of an increased number of parameters. In particular, parameters in these
methods are superfluous for relations that are either symmetric or
antisymmetric. To mitigate this problem, we propose a new L1 regularizer for
Complex Embeddings, which is one of the state-of-the-art embedding-based
methods for KBC. This regularizer promotes symmetry or antisymmetry of the
scoring function on a relation-by-relation basis, in accordance with the
observed data. Our empirical evaluation shows that the proposed method
outperforms the original Complex Embeddings and other baseline methods on the
FB15k dataset.
