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        <title>TuckER: Tensor Factorization for Knowledge Graph Completion</title>
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        <namePart type="given">Ivana</namePart>
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            <namePart type="given">Jing</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T TuckER: Tensor Factorization for Knowledge Graph Completion
%A Balazevic, Ivana
%A Allen, Carl
%A Hospedales, Timothy
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F balazevic-etal-2019-tucker
%X 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.
%R 10.18653/v1/D19-1522
%U https://aclanthology.org/D19-1522
%U https://doi.org/10.18653/v1/D19-1522
%P 5185-5194

Markdown (Informal)

[TuckER: Tensor Factorization for Knowledge Graph Completion](https://aclanthology.org/D19-1522) (Balazevic et al., EMNLP-IJCNLP 2019)

• TuckER: Tensor Factorization for Knowledge Graph Completion (Balazevic et al., EMNLP-IJCNLP 2019)

ACL

• Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019. TuckER: Tensor Factorization for Knowledge Graph Completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5185–5194, Hong Kong, China. Association for Computational Linguistics.