  Knowledge graph embedding aims to predict the missing relations between
entities in knowledge graphs. Tensor-decomposition-based models, such as
ComplEx, provide a good trade-off between efficiency and expressiveness, that
is crucial because of the large size of real world knowledge graphs. The recent
multi-partition embedding interaction (MEI) model subsumes these models by
using the block term tensor format and provides a systematic solution for the
trade-off. However, MEI has several drawbacks, some of which carried from its
subsumed tensor-decomposition-based models. In this paper, we address these
drawbacks and introduce the Multi-partition Embedding Interaction iMproved
beyond block term format (MEIM) model, with independent core tensor for
ensemble effects and soft orthogonality for max-rank mapping, in addition to
multi-partition embedding. MEIM improves expressiveness while still being
highly efficient, helping it to outperform strong baselines and achieve
state-of-the-art results on difficult link prediction benchmarks using fairly
small embedding sizes. The source code is released at
https://github.com/tranhungnghiep/MEIM-KGE.
