MEIM-KGE

Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensordecomposition-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 multipartition 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.