K-RET: knowledgeable biomedical relation extraction system

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 X 10(-12). Availability and implementation: https://github.com/lasigeBioTM/K-RET.

Automated summary

This study developed a novel biomedical relation extraction system called K-RET, which improves performance by injecting knowledge about different types of associations, multiple sources, and multi-token entities. Testing on three independent open-access corpora showed that K-RET achieved an average improvement of 2.68%. The most significant boost in performance was observed in the DDI Corpus, with a F-measure increase from 79.30% to 87.19%.