A chatbot that knows it all

A chatbot that knows it all

Problem being addressed

Diffculty in seeing a doctor​,​ long queuing time​,​ and inconvenience of making appointments have long been hurdles facing patients when they try to access primary care services.


Based on the chatbot framework, the researchers build an online question-and-answer Healthcare Helper system for answering complex medical questions. It maintains a knowledge graph constructed from medical data collected from the Internet. It also implements a novel text representation and similarity deep learning model to find the most similar question from a large question-and-answer dataset.

Advantages of this solution

The chatbot framework based on a knowledge graph and a text representation and similarity model; the advantage of the knowledge graph lies in that it utilizes structured storage so that it may help easy maintenance and retrieval of domain-specific knowledge, while the advantage of the attention model utilizes deep learning to represent better and comprehend natural language questions. Therefore, the researchers develop a system that combines the advantages of both models by integrating a knowledge graph with a neural-based model.

Solution originally applied in these industries


Telecommunications Industry

Possible New Application of the Work


Healthcare Sector

There is big potential of chatbot technologies to play a much more significant role in the medical domain. For example, a software chatbot can be deployed in the real-world to become home healthcare robots or hospital medical inquiry robots. Besides, it is possible to combine the data mining method and predict the potential diseases in a region of the population.


Internet Industry

One novelty of the work lies in the utilization of a hybrid question-and-answer model that combines a knowledge graph database and an NLP model. If it cannot find any result, a text similarity model will be used to fund the answers from a large medical question-and-answer dataset. This can be applied in complex online queries to optimise the search results.

Author of original research described in this blitzcard: Qiming Bao, Lin Ni, Jiamou Liu


Name of the author who conducted the original research that this blitzcard is based on.

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