Abstract
| Original language | English |
|---|---|
| Title of host publication | The 2018 Conference of the North American Chapter of the Association for Computational Linguistics |
| Subtitle of host publication | Human Language Technologies, Proceedings of the Conference Volume 2 (Short Papers) |
| Editors | Heng Ji, Amanda Stent |
| Place of Publication | Stroudsburg PA USA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 327–333 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781948087292 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | North American Association for Computational Linguistics 2018: Human Language Technologies - Hyatt Regency, New Orleans, United States of America Duration: 1 Jun 2018 → 6 Jun 2018 Conference number: 16th http://naacl2018.org/ |
Conference
| Conference | North American Association for Computational Linguistics 2018 |
|---|---|
| Abbreviated title | NAACL HLT 2018 |
| Country | United States of America |
| City | New Orleans |
| Period | 1/06/18 → 6/06/18 |
| Internet address |
Cite this
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A novel embedding model for knowledge base completion based on convolutional neural network. / Quoc Nguyen, Dai; Nguyen, Tu Dinh; Nguyen, Dat Quoc; Phung, Dinh.
The 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference Volume 2 (Short Papers). ed. / Heng Ji; Amanda Stent. Stroudsburg PA USA : Association for Computational Linguistics (ACL), 2018. p. 327–333.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - A novel embedding model for knowledge base completion based on convolutional neural network
AU - Quoc Nguyen, Dai
AU - Nguyen, Tu Dinh
AU - Nguyen, Dat Quoc
AU - Phung, Dinh
PY - 2018
Y1 - 2018
N2 - 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. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.
AB - 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. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.
U2 - 10.18653/v1/N18-2053
DO - 10.18653/v1/N18-2053
M3 - Conference Paper
SP - 327
EP - 333
BT - The 2018 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Ji, Heng
A2 - Stent, Amanda
PB - Association for Computational Linguistics (ACL)
CY - Stroudsburg PA USA
ER -