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arXiv · 1901.09590

TuckER: Tensor Factorization for Knowledge Graph Completion

Ivana Balažević·Carl Allen·Timothy M. Hospedales
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Abstract

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.

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Ivana Balažević, Carl Allen, Timothy M. Hospedales. 2019-01-28. TuckER: Tensor Factorization for Knowledge Graph Completion. https://doi.org/10.18653/v1/d19-1522

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