TuckER: Tensor Factorization for Knowledge Graph Completion

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Authors Timothy M. Hospedales, Ivana Balažević, Carl Allen
Journal/Conference Name IJCNLP 2019 11
Paper Category
Paper 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.
Date of publication 2019
Code Programming Language Multiple
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