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This paper presents a translation-based knowledge geraph embedding method viaefficient relation rotation (TransERR), a straightforward yet effectivealternative to traditional translation-based knowledge graph embedding models.Different from the previous translation-based models, TransERR encodesknowledge graphs in the hypercomplex-valued space, thus enabling it to possessa higher degree of translation freedom in mining latent information between thehead and tail entities. To further minimize the translation distance, TransERRadaptively rotates the head entity and the tail entity with their correspondingunit quaternions, which are learnable in model training. We also providemathematical proofs to demonstrate the ability of TransERR in modeling variousrelation patterns, including symmetry, antisymmetry, inversion, composition,and subrelation patterns. The experiments on 10 benchmark datasets validate theeffectiveness and the generalization of TransERR. The results also indicatethat TransERR can better encode large-scale datasets with fewer parameters thanthe previous translation-based models. Our code and datasets are availableat~\url{https://github.com/dellixx/TransERR}.

TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation