{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T21:14:11Z","timestamp":1790889251620,"version":"4.1.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:p>Relation extraction is one of the core tasks of natural language processing, which aims to identify entities in unstructured text and judge the semantic relationships between them. In the traditional methods, the extraction of rich features and the judgment of complex semantic relations are inadequate. Therefore, in this paper, we propose a relation extraction model, HAGCN, based on heterogeneous graph convolutional neural network and graph attention mechanism. We have constructed two different types of nodes, words and relations, in a heterogeneous graph convolutional neural network, which are used to extract different semantic types and attributes and further extract contextual semantic representations. By incorporating the graph attention mechanism to distinguish the importance of different information, and the model has stronger representation ability. In addition, an information update mechanism is designed in the model. Relation extraction is performed after iteratively fusing the node semantic information to obtain a more comprehensive node representation. The experimental results show that the HAGCN model achieves good relation extraction performance, and its F1 value reaches 91.51% in the SemEval-2010 Task 8 dataset. In addition, the HAGCN model also has good results in the WebNLG dataset, verifying the generalization ability of the model.<\/jats:p>","DOI":"10.3233\/ida-240083","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T13:06:02Z","timestamp":1719579962000},"page":"257-270","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["HAGCN: A relation extraction model based on heterogeneous graph convolutional neural network and graph attention"],"prefix":"10.1177","volume":"29","author":[{"given":"Xinyu","family":"He","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, Liaoning, China"},{"name":"Information and Communication Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China"},{"name":"Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manfei","family":"Kan","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, Liaoning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggong","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, Liaoning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neunet.2020.10.012","article-title":"Distant supervision relation extraction via adaptive dependency-path and additional knowledge graph supervision","volume":"134","author":"Shi Y.","year":"2021","unstructured":"Shi Y. et\u00a0al., Distant supervision relation extraction via adaptive dependency-path and additional knowledge graph supervision, Neural Networks 134 (2021), 42\u201353.","journal-title":"Neural Networks"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Li M. 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