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

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dc.contributor.author Bao, Qiming
dc.contributor.author Ni, Lin
dc.contributor.author Liu, Jiamou
dc.coverage.spatial Swinburne Univ Technol, Melbourne, AUSTRALIA
dc.date.accessioned 2021-12-06T00:31:43Z
dc.date.available 2021-12-06T00:31:43Z
dc.date.issued 2020-1-29
dc.identifier.isbn 9781450376976
dc.identifier.issn 2153-1633
dc.identifier.uri https://hdl.handle.net/2292/57635
dc.description.abstract 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.
dc.publisher ACM
dc.relation.ispartof ACSW '20: Australasian Computer Science Week 2020
dc.relation.ispartofseries Proceedings of the Australasian Computer Science Week Multiconference
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.subject Science & Technology
dc.subject Technology
dc.subject Computer Science, Theory & Methods
dc.subject Computer Science
dc.subject Hierarchial BiLSTM attention model
dc.subject natural language processing
dc.subject knowledge graph
dc.subject question answering
dc.subject medical chatbot
dc.subject cs.CL
dc.subject cs.CL
dc.title HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention
dc.type Conference Item
dc.identifier.doi 10.1145/3373017.3373049
pubs.begin-page 1
dc.date.updated 2021-11-29T03:30:50Z
dc.rights.holder Copyright: The author en
pubs.author-url http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000571662300034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d
pubs.end-page 10
pubs.finish-date 2020-2-7
pubs.publication-status Published
pubs.start-date 2020-2-3
dc.rights.accessrights http://purl.org/eprint/accessRights/RetrictedAccess en
pubs.subtype Proceedings
pubs.elements-id 796364
pubs.online-publication-date 2020-1-29

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