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Papers/TuckER: Tensor Factorization for Knowledge Graph Completion

TuckER: Tensor Factorization for Knowledge Graph Completion

Ivana Balažević, Carl Allen, Timothy M. Hospedales

2019-01-28IJCNLP 2019 11Knowledge GraphsKnowledge Graph CompletionPredictionLink Prediction
PaperPDFCode(official)CodeCodeCodeCode

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.

Results

TaskDatasetMetricValueModel
Link Prediction FB15kHits@10.741TuckER
Link Prediction FB15kHits@100.892TuckER
Link Prediction FB15kHits@30.833TuckER
Link Prediction FB15kMRR0.795TuckER
Link PredictionWN18RRHits@10.443TuckER
Link PredictionWN18RRHits@100.526TuckER
Link PredictionWN18RRHits@30.482TuckER
Link PredictionWN18RRMRR0.47TuckER
Link PredictionWN18Hits@10.949TuckER
Link PredictionWN18Hits@100.958TuckER
Link PredictionWN18Hits@30.955TuckER
Link PredictionWN18MRR0.953TuckER
Link PredictionFB15k-237Hits@10.266TuckER
Link PredictionFB15k-237Hits@100.544TuckER
Link PredictionFB15k-237Hits@30.394TuckER
Link PredictionFB15k-237MRR0.358TuckER

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