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This paper presents translation-based knowledge graph completion method viaefficient relation rotation (TransERR), a straightforward yet effectivealternative to traditional translation-based knowledge graph completion 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. The experiments on 7benchmark datasets validate the effectiveness and the generalization ofTransERR. The results also indicate that TransERR can better encode large-scaledatasets with fewer parameters than the previous translation-based models. Ourcode is available at: \url{https://github.com/dellixx/TransERR}.

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