KBMQA: medical question and answering model based on Knowledge Graph and BERT

Abstract

The emergence of BERT and Knowledge Graph (KG) has promoted the development of Question Answering (QA), however, existing QA systems are still inadequate in terms of the accuracy of relational reasoning and the interpretability of answers. In this paper, we combine BERT and KG, following and optimizing existing methods to build a medical QA system with better performance - KBMQA. The final experimental results show that KBMQA performs better on both MedQA and MedNLI datasets compared with previous biomedical baseline models and MOP models.