  Many graph embedding approaches have been proposed for knowledge graph
completion via link prediction. Among those, translating embedding approaches
enjoy the advantages of light-weight structure, high efficiency and great
interpretability. Especially when extended to complex vector space, they show
the capability in handling various relation patterns including symmetry,
antisymmetry, inversion and composition. However, previous translating
embedding approaches defined in complex vector space suffer from two main
issues: 1) representing and modeling capacities of the model are limited by the
translation function with rigorous multiplication of two complex numbers; and
2) embedding ambiguity caused by one-to-many relations is not explicitly
alleviated. In this paper, we propose a relation-adaptive translation function
built upon a novel weighted product in complex space, where the weights are
learnable, relation-specific and independent to embedding size. The translation
function only requires eight more scalar parameters each relation, but improves
expressive power and alleviates embedding ambiguity problem. Based on the
function, we then present our Relation-adaptive translating Embedding (RatE)
approach to score each graph triple. Moreover, a novel negative sampling method
is proposed to utilize both prior knowledge and self-adversarial learning for
effective optimization. Experiments verify RatE achieves state-of-the-art
performance on four link prediction benchmarks.
