基于图卷积网络的多源本体相似度计算方法

在信息时代，数据量呈指数式增长，而不同数据源存在难以统一表示的异构问题，给数据共享、重用造成不便。语义网络的迅速发展，使本体映射成为解决该问题的有效手段，其核心是本体相似度计算，提出了一种基于图卷积网络的计算方法。将本体建模为异构图网络，再使用图卷积网络学习文本嵌入规则，得到全局统一表示，完成多源数据的融合。实验结果表明，所提方法计算准确性高于其他传统方法，有效地提高了多源数据融合的准确度。

Novel similarity calculation method of multisource ontology based on graph convolution network

In the information age, the amount of data is growing exponentially.However, different data sources are heterogeneous, which makes it inconvenient to share and multiplex data.With the rapid development of semantic network, ontology mapping is an effective method to solve this problem.The core of ontology mapping is ontology similarity calculation.Therefore, a calculation method based on graph convolution network was proposed.Firstly, ontologiesare modeled as a heterogeneous graph network, then the graph convolution network was used to learn the text embedding rules, which made ontologies were definedin global unified representation.Lastly, multisource data fusion was completed.The experimental results show that the accuracy of the proposed method is higher than other methods, and the accuracy of multi-source data fusion was effectively improved.