Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion
This addresses the problem of linking equivalent entities across patent data sources for researchers and practitioners, though it appears incremental as it combines existing techniques for a specific domain.
The paper tackles entity alignment in science and technology patent knowledge graphs by proposing a method that fuses structural and attribute information using graph convolution networks and BERT, improving performance as shown by better Hit@K metrics on three benchmark datasets.
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.