K-RET: knowledgeable biomedical relation extraction system
Abstract Motivation Relation 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. Results We 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.
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain
- Grant/Contract Number:
- PTDC/CCIBIO/28685/2017
- OSTI ID:
- 1970408
- Journal Information:
- Bioinformatics, Journal Name: Bioinformatics Vol. 39 Journal Issue: 4; ISSN 1367-4811
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span
An evaluation of GPT models for phenotype concept recognition