人工智能
机器学习
自然语言处理
任务(项目管理)
推论
语义学(计算机科学)
特征学习
作者
Yingying Zhang,Shengsheng Qian,Quan Fang,Changsheng Xu
出处
期刊:ACM Multimedia
日期:2019-10-15
卷期号:: 1089-1097
被引量:10
标识
DOI:10.1145/3343031.3351033
摘要
Online healthcare services can offer public ubiquitous access to the medical knowledge, especially with the emergence of medical question answering websites, where patients can get in touch with doctors without going to hospital. Explainability and accuracy are two main concerns for medical question answering. However, existing methods mainly focus on accuracy and cannot provide a good explanation for retrieved medical answers. This paper proposes a novelMulti-Modal Knowledge-aware Hierarchical Attention Network (MKHAN) to effectively exploit multi-modal knowledge graph (MKG) for explainable medical question answering. MKHAN can generate path representation by composing the structural, linguistics, and visual information of entities, and infer the underlying rationale of question-answer interactions by leveraging the sequential dependencies within a path from MKG. Furthermore, a novel hierarchical attention network is proposed to discriminate the salience of paths endowing our model with explainability. We build a large-scale multi-modal medical knowledge graph andtwo real-world medical question answering datasets, the experimental results demonstrate the superior performance on our approachcompared with the state-of-the-art methods.
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