TY  - JOUR
T1  - Knowledge Graph Embedding for Link Prediction using Node-Link Interaction-based Graph Attention Networks
AU  - Kim, Junseon 
AU  - Kim, Myoungho 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.7.555
KW  - knowledge graph embedding
KW  - knowledge graph representation
KW  - graph neural network
KW  - graph embedding
KW  - link prediction
AB  - Knowledge graphs are structures that express knowledge in the real world in the form of nodes and links-based triple form. These knowledge graphs are incomplete and many embedding techniques have been studied to effectively represent nodes and links in low-dimensional vector spaces to find other missing relationships. Recently, many neural network-based knowledge graph link prediction methods have been studied. However existing models consider nodes and links independently when determining the importance of a triple to a node which makes it difficult to reflect the interaction between nodes and links. In this paper, we propose an embedding method that will be used to analyze the importance of triple units by simultaneously considering nodes and links using composition operators, and at the same time prove that the model outperforms other methods in knowledge graph link prediction.