TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple yet powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. By using this particular decomposition, parameters are shared between relations, enabling multi-task learning. TuckER outperforms previous state-of-the-art models across several standard link prediction datasets.
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Knowledge Graph CompletionKnowledge GraphsLink PredictionMulti-Task LearningPredictionSimilar Papers 제목 키워드 기반
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