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

Zhangkui Liu; Tao Wu

doi:10.1117/12.2685841

15 August 2023 KBMQA: medical question and answering model based on Knowledge Graph and BERT

Zhangkui Liu, Tao Wu

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127191K (2023) https://doi.org/10.1117/12.2685841
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

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.

Citation Download Citation

Zhangkui Liu and Tao Wu "KBMQA: medical question and answering model based on Knowledge Graph and BERT", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127191K (15 August 2023); https://doi.org/10.1117/12.2685841

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