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Existing methods face limitations when handling non\u2010local and multi\u2010layered semantic dependencies, making it difficult to effectively integrate global semantics with local interactions. The goal of this study is to propose a novel model to address this issue and enhance the ability to model complex dependencies. This paper proposes a new model that combines global dependency graphs with multi\u2010level semantic information graphs (DMK). By utilizing a dual\u2010graph collaborative mechanism, it integrates document\u2010level contextual information to accurately model complex dependencies between entities. We introduce the KanChebConv convolutional layer based on the Kolmogorov\u2013Arnold Network (KAN), replacing traditional linear weight matrices with learnable spline functions, thereby enhancing the model's ability to capture non\u2010linear dependencies. We evaluated our model on the chemical\u2013disease relation (CDR) dataset and the gene\u2013disease relation (GDA) dataset. The results demonstrate that our model achieved the highest F1 score among the selected baselines on both datasets, thereby validating its robustness and competitiveness. Through the collaborative mechanism of global and local information and the innovative KAN convolutional layer, our model effectively improves the accuracy and robustness of document\u2010level biomedical relation extraction, showcasing strong potential for practical applications.<\/jats:p>","DOI":"10.1002\/cpe.70551","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T06:03:56Z","timestamp":1767679436000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Global to Local: A Dependency and Semantic Integration\u2010Based Document\u2010Level Biomedical Relation Extraction Method"],"prefix":"10.1002","volume":"38","author":[{"given":"Bin","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Shandong University of Technology  Zibo Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingchuan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Shandong University of Technology  Zibo Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Che","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering and Food Science Shandong University of Technology  Zibo Shandong China"},{"name":"Xi'an Aeronautics Computing Technique Research Institute  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Shandong University of Technology  Zibo Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer Science Northwestern Polytechnical University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2345-0026","authenticated-orcid":false,"given":"Linlin","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Shandong University of Technology  Zibo Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"B.Yu Z.Zhang T.Liu B.Wang S.Li andQ.Li \u201cBeyond Word Attention: Using Segment Attention in Neural Relation Extraction \u201dinProceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI\u201019) 2019 5401\u20135407 https:\/\/doi.org\/10.24963\/ijcai.2019\/750.","DOI":"10.24963\/ijcai.2019\/750"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"S.WuandY.He \u201cEnriching Pre\u2010Trained Language Model With Entity Information for Relation Classification \u201dinProceedings of the 28th ACM International Conference on Information and Knowledge Management 2019 2361\u20132364 https:\/\/doi.org\/10.1145\/3357384.3358119.","DOI":"10.1145\/3357384.3358119"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Y.Zhang P.Qi andC. 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