Entity Alignment Method of Science and Technology Patent based on Graph
  Convolution Network and Information Fusion
Runze Fang Yawen Li Yingxia Shao Zeli Guan Zhe Xue
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.