Learning Entity and Relation Embeddings with Entity Description for Knowledge Graph Completion
Shaozheng Dai, Yanchun Liang, Shuyan Liu, Ying Wang, Wenle Shao, Xixun Lin, Xiaoyue Feng · 2018
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 descriptionembodied 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.