HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention
This work addresses the need for accurate medical question-answering systems, but it is incremental as it builds on existing knowledge graph and text similarity approaches.
The paper tackled the problem of answering complex medical questions by proposing HHH, an online chatbot system that combines a knowledge graph with a hierarchical BiLSTM attention model (HBAM), achieving superior performance compared to state-of-the-art methods like BERT and MaLSTM on a medical subset of the Quora dataset.
This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph constructed from medical data collected from the Internet. HHH also implements a novel text representation and similarity deep learning model, Hierarchical BiLSTM Attention Model (HBAM), to find the most similar question from a large QA dataset. We compare HBAM with other state-of-the-art language models such as bidirectional encoder representation from transformers (BERT) and Manhattan LSTM Model (MaLSTM). We train and test the models with a subset of the Quora duplicate questions dataset in the medical area. The experimental results show that our model is able to achieve a superior performance than these existing methods.