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

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)

PDF Abstract NAACL 2018 PDF NAACL 2018 Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Link Prediction FB15k-237 ConvKB MRR 0.396 # 6
Hits@10 0.517 # 28
MR 257 # 15
Evaluation Protocol Can be affected with a more appropriate protocol. See the row of ConvKB (Corrected). # 1
Link Prediction WN18RR ConvKB MRR 0.248 # 39
Hits@10 0.525 # 29
MR 2554.0 # 8

Methods used in the Paper


METHOD TYPE
Convolution
Convolutions