.png){kind=icon}
Return
Knowledge graph relation prediction based on graph transformation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-024-06080-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Knowledge graph relation prediction aims to predict the missing relation between entities. Many existing graph neural network (GNN)-based relation prediction models suffer from over-parameterization, and some models cannot effectively learn the correlation between relations for the relation prediction task. In order to solve the above problems, we propose a knowledge graph relation prediction model based on graph transformation. We use two kinds of graph transformation and a parallel fusion model to learn the semantic information, which effectively reduces the number of parameters and reduces the loss of semantic information compared to the Levi graph. Then, we utilize the self-attention mechanism to learn the correlation between relations, and combine it with the DistMult scoring function to complete the relation prediction task. Experiments on four real-world datasets WN18RR, CoDEx-S, Kinship, and FB15K-237 show that our model achieved a better balance between the number of parameters and prediction performance compared to existing GNN-based models on most datasets.
Keywords:
Knowledge graph relation prediction
Graph transformation
Graph neural network
Relation correlation
Journal
IF:
3.5
Papers:
7.6K
Citations:
1.7W
Organization
Cited Papers
ShareSaveHierarchical-aware relation rotational knowledge graph embedding for link prediction.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSave
ShareSavePRGNN: Modeling high-order proximity with relational graph neural network for knowledge graph completionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSave
View more