TuckER: Tensor Factorization for Knowledge Graph Completion
This addresses the issue of incomplete knowledge graphs for AI applications, providing a strong baseline for more complex models.
The paper tackled the problem of link prediction in knowledge graphs by proposing TuckER, a linear model based on Tucker decomposition, which outperformed previous state-of-the-art models across standard datasets.
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.