  Knowledge graph embedding methods are important for the knowledge graph
completion (or link prediction) task. One existing efficient method, PairRE,
leverages two separate vectors to model complex relations (i.e., 1-to-N,
N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly
restricts entities on the hyper-ellipsoid surfaces which limits the
optimization of entity distribution, leading to suboptimal performance of
knowledge graph completion. To address this issue, we propose a novel score
function TranSHER, which leverages relation-specific translations between head
and tail entities to relax the constraint of hyper-ellipsoid restrictions. By
introducing an intuitive and simple relation-specific translation, TranSHER can
provide more direct guidance on optimization and capture more semantic
characteristics of entities with complex relations. Experimental results show
that TranSHER achieves significant performance improvements on link prediction
and generalizes well to datasets in different domains and scales. Our codes are
public available at https://github.com/yizhilll/TranSHER.
