BioGSF: a graph-driven semantic feature integration framework for biomedical relation extraction.

ABSTRACT: The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections. In this study, we presented a graph-driven framework named BioGSF for RE from the literature by integrating shortest dependency paths (SDP) with entity-pair graph through the employment of the graph neural netwo

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11759886 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature