High-throughput biomedical relation extraction for semi-structured web articles empowered by large language models.

ABSTRACT:

Background

We aim to develop a high-throughput biomedical relation extraction system tailored for semi-structured biomedical websites, leveraging the reading comprehension abilities and domain-specific medical knowledge of large language models (LLMs).

Methods

We formulate relation extraction as a series of binary classification problems. Given the context from semi-structured biomedical web articles, LLMs decide whether a relation holds while providing accompanying rationales for factual verification. The article's main title is designated as the tail entity, and candidate head entities are identified by matching against a biomedical thesaurus with semantic typing to guide candidate relation types. To assess system performance and robustness, we compare general-purpose, dom

SUBMITTER: Zhou S 

PROVIDER: S-EPMC12482089 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature