# HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention

arXiv: https://arxiv.org/abs/2002.03140 · HTML: https://mlsanity.com/paper/2002.03140/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation](https://mlsanity.com/paper/2207.14000/) (2022)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning](https://mlsanity.com/paper/2305.12599/) (2023)
- [Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages](https://mlsanity.com/paper/2302.09685/) (2023)
- [Natural Language Processing in Customer Service: A Systematic Review](https://mlsanity.com/paper/2212.09523/) (2022)
- [High Order Reasoning for Time Critical Recommendation in Evidence-based Medicine](https://mlsanity.com/paper/2405.03010/) (2024)

## Abstract (arXiv)

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.