A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

NAACL 2018 Dai Quoc NguyenTu Dinh NguyenDat Quoc NguyenDinh Phung

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Link Prediction WN18RR ConvKB MRR 0.248 # 9
Link Prediction WN18RR ConvKB [email protected] 0.525 # 6
Link Prediction WN18RR ConvKB MR 2554 # 2