ArticleBioinformatics (Oxford, England)2023
K-RET: knowledgeable biomedical relation extraction system.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Knowledge-augmented pre-trained language models for biomedical relation extraction.BMC bioinformatics · 2025Article
- Biomedical relation extraction method based on ensemble learning and attention mechanism.BMC bioinformatics · 2024Article
- The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.Database : the journal of biological databases and curation · 2024Article
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2 authors.
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Abstract
motivationRelation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical RE system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities.
resultsWe tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a P-value of 2.91×10-12. AVAILABILITY AND IMPLEMENTATION: https://github.com/lasigeBioTM/K-RET.
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