  Knowledge graphs are structured representations of real world facts. However,
they typically contain only a small subset of all possible facts. Link
prediction is a task of inferring missing facts based on existing ones. We
propose TuckER, a relatively straightforward but powerful linear model based on
Tucker decomposition of the binary tensor representation of knowledge graph
triples. TuckER outperforms previous state-of-the-art models across standard
link prediction datasets, acting as a strong baseline for more elaborate
models. We show that TuckER is a fully expressive model, derive sufficient
bounds on its embedding dimensionalities and demonstrate that several
previously introduced linear models can be viewed as special cases of TuckER.
