Similarity Computation of Heterogeneous Ontology Based on Graph Attention Network

Abstract

Ontologies are crucial for data integration and information sharing. However, due to the different knowledge backgrounds of domain and ontology developers, the heterogeneity problem of multi-source ontology existence is more prominent, and ontology mapping is an important way to solve the ontology heterogeneity problem. However, the ontology similarity calculation methods among them still need to be improved in terms of accuracy or stability. In this paper, we propose an ontology similarity calculation method based on graph attention networks, which models ontologies as heterogeneous graph networks and uses the graph attention network model to introduce an attention mechanism to dynamically consider the influence of edge weights to achieve neighbor aggregation and perform similarity calculation. The experimental results show that this method has higher accuracy than the existing ontology similarity calculation methods.