  Semantic matching models -- which assume that entities with similar semantics
have similar embeddings -- have shown great power in knowledge graph embeddings
(KGE). Many existing semantic matching models use inner products in embedding
spaces to measure the plausibility of triples and quadruples in static and
temporal knowledge graphs. However, vectors that have the same inner products
with another vector can still be orthogonal to each other, which implies that
entities with similar semantics may have dissimilar embeddings. This property
of inner products significantly limits the performance of semantic matching
models. To address this challenge, we propose a novel regularizer -- namely,
DUality-induced RegulArizer (DURA) -- which effectively encourages the entities
with similar semantics to have similar embeddings. The major novelty of DURA is
based on the observation that, for an existing semantic matching KGE model
(primal), there is often another distance based KGE model (dual) closely
associated with it, which can be used as effective constraints for entity
embeddings. Experiments demonstrate that DURA consistently and significantly
improves the performance of state-of-the-art semantic matching models on both
static and temporal knowledge graph benchmarks.
