[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81486-en":3,"doc-seo-81486-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81486,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Entity Alignment Method of Science and Technology Patent Based on Graph Convolution Network and Information Fusion","The entity alignment of science and technology patents links equivalent entities across knowledge graphs built from different patent data sources. Many existing approaches rely only on graph neural network structure embeddings or attribute-text semantic representations, overlooking multi-information fusion inherent in patent entities. This paper presents an entity alignment method integrating graph convolution networks with a BERT-based representation pipeline to embed graph structure and entity attributes, achieving effective multi-information fusion. Experiments on three benchmark datasets show improved Hits@k performance over prior methods.","Entity Alignment Method of Science and Technology Patent Based on Graph Convolution Network and Information Fusion  \nRunze Fang  \nBeijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications Beijing, China  \nYawen Li∗ [warmly0716@126.com](warmly0716@126.com)[ ](warmly0716@126.com)School of Economics and Management, Beijing University of Posts and Telecommunications Beijing, China  \nYingxia Shao  \nBeijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications Beijing, China  \narXiv :2311 .00300v2 [ cs .CL] 10 Jul 2026  \nZeli Guan  \nBeijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications Beijing, China  \nZhe Xue  \nBeijing Key Laboratory of Intelligent Communication Software and Multimedia, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications Beijing, China  \nAbstract  \nThe 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 has better Hits@􀀠 evaluation indicators than existing methods.  \nKeywords  \nknowledge graph, entity alignment, science and technology patent, information fusion, graph convolution network  \n1 Introduction  \nWith the rapid development of science and technology and knowledge map research [1], a large number of science and technology patents have emerged. Broader innovation studies also indicate that scientific and technological resources interact with education,  \n∗ Corresponding author.  \nidentity and creative capacity in innovation processes [2] . Scientific resources are often multi-view and dynamic: scholar clustering work has shown that research interests can evolve across views [3], and scientific information retrieval must handle both semantic and media heterogeneity [4] . However, at present, many knowledge graphs [5] related to science and technology patents are constructed by different institutions and individuals. Large graph processing also faces memory and scalability constraints, as shown by secondorder random walk research on billion-edge natural graphs [6] . The requirements of these knowledge graphs are specific, and the design and construction are not uniform [7], so there are problems of heterogeneity and redundancy among them. The entity alignment of science and technology patents is the key technology in the process of knowledge fusion of science and technology patents. The main purpose is to find the equivalent entities between different science and technology patent data. Because the knowledge content of different science and technology patent data has different sources and human understanding, the text expression of t","cbCaiimMzMxGA8Ff","https://ap.wps.com/l/cbCaiimMzMxGA8Ff","pdf",474210,2,1,"English","en",105,"# Introduction\n## Background and problem of patent knowledge graph heterogeneity\n## Related work on entity alignment methods\n## Motivation for graph convolution and information fusion","[{\"question\":\"What is the goal of entity alignment for science and technology patents?\",\"answer\":\"It aims to identify equivalent entities across knowledge graphs derived from different science and technology patent data sources.\"},{\"question\":\"Why do existing entity alignment methods have limitations?\",\"answer\":\"They often use only graph-structure embeddings or only attribute-text semantics, failing to fully incorporate multi-information fusion from patent entities.\"},{\"question\":\"How does the proposed method improve entity alignment performance?\",\"answer\":\"It uses a graph convolution network together with a BERT model to embed and fuse knowledge-graph structure information and entity attribute 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