Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion
- DOI
- 10.2991/icaita-18.2018.49How to use a DOI?
- Keywords
- knowledge graph completion; entity description; natural language processing
- Abstract
With the growth of existing knowledge graph, the completion of knowledge graph has become a crucial problem. In this paper, we propose a novel model based on description-embodied knowledge representation learning framework, which is able to take advantages of both fact triples and entity description. Specifically, the relation projection is combined with description-embodied representation learning to learn entity and relation embeddings. Convolutional neural network and TransR are adopted to get the description-based and structure-based representation of entity and relation, respectively. We employ FB15K dataset generated from a large knowledge graph freebase, to evaluate the performances of the proposed model. Experimental results show that our proposed model greatly outperforms other existing baseline models.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Shaozhi Dai AU - Yanchun Liang AU - Shuyan Liu AU - Ying Wang AU - Wenle Shao AU - Xixun Lin AU - Xiaoyue Feng PY - 2018/03 DA - 2018/03 TI - Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion BT - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018) PB - Atlantis Press SP - 194 EP - 197 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-18.2018.49 DO - 10.2991/icaita-18.2018.49 ID - Dai2018/03 ER -