From Global to Local: A Dependency and Semantic Integration‐Based Document‐Level Biomedical Relation Extraction Method

ABSTRACT Document level biomedical relation extraction aims to identify complex relationships between entity pairs in biomedical literature, which is crucial for the automation of medical knowledge applications. Existing methods face limitations when handling non‐local and multi‐layered 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‐level semantic information graphs (DMK). By utilizing a dual‐graph collaborative mechanism, it integrates document‐level contextual information to accurately model complex dependencies between entities. We introduce the KanChebConv convolutional layer based on the Kolmogorov–Arnold Network (KAN), replacing traditional linear weight matrices with learnable spline functions, thereby enhancing the model's ability to capture non‐linear dependencies. We evaluated our model on the chemical–disease relation (CDR) dataset and the gene–disease 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‐level biomedical relation extraction, showcasing strong potential for practical applications.