TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
International Conference on Language Resources and Evaluation(2023)
Abstract
This paper presents a translation-based knowledge geraph embedding method via
efficient relation rotation (TransERR), a straightforward yet effective
alternative to traditional translation-based knowledge graph embedding models.
Different from the previous translation-based models, TransERR encodes
knowledge graphs in the hypercomplex-valued space, thus enabling it to possess
a higher degree of translation freedom in mining latent information between the
head and tail entities. To further minimize the translation distance, TransERR
adaptively rotates the head entity and the tail entity with their corresponding
unit quaternions, which are learnable in model training. We also provide
mathematical proofs to demonstrate the ability of TransERR in modeling various
relation patterns, including symmetry, antisymmetry, inversion, composition,
and subrelation patterns. The experiments on 10 benchmark datasets validate the
effectiveness and the generalization of TransERR. The results also indicate
that TransERR can better encode large-scale datasets with fewer parameters than
the previous translation-based models. Our code and datasets are available
at .
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