.png){kind=icon}
Return
An effective knowledge graph entity alignment model based on multiple information
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2023.02.029.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Entity alignment refers to matching entities with the same realistic meaning in different knowledge graphs. The structure of a knowledge graph provides the global signal for entity alignment. But in the real world, a knowledge graph provides insufficient structural information in general. Moreover, the problem of knowledge graph heterogeneity is common. The semantic and string information can alleviate the problems caused by the sparse and heterogeneous nature of knowledge graphs, yet both of them have not been fully utilized by most existing work. Therefore, we propose an entity alignment model based on multiple information (EAMI), which employs structural, semantic and string information. EAMI learns the structural representation of a knowledge graph by using multi-layer graph convolutional networks. To acquire more accurate entity vector representation, we incorporate the attribute semantic representation into the structural representation. In addition, to further improve entity alignment, we study the entity name string information. There is no training required to calculate the similarity of entity names. Our model is tested on publicly available cross-lingual datasets and cross-resource datasets, and the experimental results demonstrate the effectiveness of our model.(c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Entity alignment
Knowledge graph
Structure
Semantic
String
Journal
IF:
6.3
Papers:
8.3K
Citations:
3.0W
Organization
Cited Papers
Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
ShareSave
ShareSaveShareSaveShareSaveShareSaveDegree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graphNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more