Entity alignment plays a crucial role in the automatic fusion of multi-source heterogeneous petroleum data asset knowledge graphs.Currently,mainstream entity alignment models based on graph neural networks mainly focus on the information of entities and graph structures,however,they ignore the semantic information of multi-neighborhoods,such as the relationships between entities,attributes,and attribute values.Their performance in the fusion process of data asset knowledge graphs in the petroleum field with significant differences in naming rules and multiple industry-specific semantic entities is average.This study proposes an improved Multi-Neighborhood Awareness Network(MNAN)model based on graph attention network for entity alignment.By using a BERT-based multi-lingual pre-training model to obtain initial features of entities and multi-neighborhoods,a graph convolutional neural network with highway networks is used to aggregate neighborhood entities and graph structural features.Multi-neighborhood perception and entity enhancement are used to aggregate multi-neighborhood features of entities in the attention network.Finally,the model is trained using a minimum marginal-based loss function.An entity alignment experiment is conducted on two knowledge graphs in the data asset knowledge graph dataset of the petroleum field.The experimental results show that the MNAN model outperforms all compared entity alignment models based on the graph neural network,Hits@1 value reaches 86.7%,which is approximately 2.3 percentange points better than that of the best performing comparative model.
entity alignmentmulti-neighborhood awarenessgraph attention networkdata assets in the petroleum fieldKnowledge Graph(KG)
国家科技期刊平台
NSTL
维普
万方数据
