  The scoring function, which measures the plausibility of triplets in
knowledge graphs (KGs), is the key to ensure the excellent performance of KG
embedding, and its design is also an important problem in the literature.
Automated machine learning (AutoML) techniques have recently been introduced
into KG to design task-aware scoring functions, which achieve state-of-the-art
performance in KG embedding. However, the effectiveness of searched scoring
functions is still not as good as desired. In this paper, observing that
existing scoring functions can exhibit distinct performance on different
semantic patterns, we are motivated to explore such semantics by searching
relation-aware scoring functions. But the relation-aware search requires a much
larger search space than the previous one. Hence, we propose to encode the
space as a supernet and propose an efficient alternative minimization algorithm
to search through the supernet in a one-shot manner. Finally, experimental
results on benchmark datasets demonstrate that the proposed method can
efficiently search relation-aware scoring functions, and achieve better
embedding performance than state-of-the-art methods.
