  Knowledge graph embedding has been an active research topic for knowledge
base completion (KGC), with progressive improvement from the initial TransE,
TransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE
ignores the multi-faceted nature of the entity and the complexity of the
relation, only using rigorous operation on quaternion space to capture the
interaction between entitiy pair and relation, leaving opportunities for better
knowledge representation which will finally help KGC. In this paper, we propose
a novel model, QuatDE, with a dynamic mapping strategy to explicitly capture
the variety of relational patterns and separate different semantic information
of the entity, using transition vectors to adjust the point position of the
entity embedding vectors in the quaternion space via Hamilton product,
enhancing the feature interaction capability between elements of the triplet.
Experiment results show QuatDE achieves state-of-the-art performance on three
well-established knowledge graph completion benchmarks. In particular, the MR
evaluation has relatively increased by 26% on WN18 and 15% on WN18RR, which
proves the generalization of QuatDE.
