The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources. Most entity alignment methods only use graph neural network to obtain the embedding of graph structure or use attribute text description to obtain semantic representation, ignoring the process of multi-information fusion in science and technology patents. In order to make use of the graphic structure and auxiliary information such as the name, description and attribute of the patent entity, this paper proposes an entity alignment method based on the graph convolution network for science and technology patent information fusion. Through the graph convolution network and BERT model, the structure information and entity attribute information of the science and technology patent knowledge graph are embedded and represented to achieve multi-information fusion, thus improving the performance of entity alignment. Experiments on three benchmark data sets show that the proposed method Hit@K The evaluation indicators are better than the existing methods.

翻译：科技专利实体对齐旨在链接不同科技专利数据源知识图谱中的等价实体。现有实体对齐方法大多仅利用图神经网络获取图谱结构嵌入，或使用属性文本描述获取语义表示，忽略了科技专利中多信息融合的过程。为综合利用图谱结构以及专利实体名称、描述、属性等辅助信息，本文提出一种基于图卷积网络的科技专利信息融合实体对齐方法。通过图卷积网络与BERT模型，对科技专利知识图谱的结构信息与实体属性信息进行嵌入表示，实现多信息融合，从而提升实体对齐性能。在三个基准数据集上的实验表明，所提方法的Hit@K评估指标均优于现有方法。