Cargando…

A knowledge graph based question answering method for medical domain

Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. I...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Xiaofeng, Zhang, Jixin, Xu, Zisang, Ou, Lu, Tong, Jianbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444078/
https://www.ncbi.nlm.nih.gov/pubmed/34604514
http://dx.doi.org/10.7717/peerj-cs.667
_version_ 1784568417699758080
author Huang, Xiaofeng
Zhang, Jixin
Xu, Zisang
Ou, Lu
Tong, Jianbin
author_facet Huang, Xiaofeng
Zhang, Jixin
Xu, Zisang
Ou, Lu
Tong, Jianbin
author_sort Huang, Xiaofeng
collection PubMed
description Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions’ intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.
format Online
Article
Text
id pubmed-8444078
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-84440782021-09-30 A knowledge graph based question answering method for medical domain Huang, Xiaofeng Zhang, Jixin Xu, Zisang Ou, Lu Tong, Jianbin PeerJ Comput Sci Artificial Intelligence Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions’ intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach. PeerJ Inc. 2021-09-01 /pmc/articles/PMC8444078/ /pubmed/34604514 http://dx.doi.org/10.7717/peerj-cs.667 Text en © 2021 Huang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Huang, Xiaofeng
Zhang, Jixin
Xu, Zisang
Ou, Lu
Tong, Jianbin
A knowledge graph based question answering method for medical domain
title A knowledge graph based question answering method for medical domain
title_full A knowledge graph based question answering method for medical domain
title_fullStr A knowledge graph based question answering method for medical domain
title_full_unstemmed A knowledge graph based question answering method for medical domain
title_short A knowledge graph based question answering method for medical domain
title_sort knowledge graph based question answering method for medical domain
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8444078/
https://www.ncbi.nlm.nih.gov/pubmed/34604514
http://dx.doi.org/10.7717/peerj-cs.667
work_keys_str_mv AT huangxiaofeng aknowledgegraphbasedquestionansweringmethodformedicaldomain
AT zhangjixin aknowledgegraphbasedquestionansweringmethodformedicaldomain
AT xuzisang aknowledgegraphbasedquestionansweringmethodformedicaldomain
AT oulu aknowledgegraphbasedquestionansweringmethodformedicaldomain
AT tongjianbin aknowledgegraphbasedquestionansweringmethodformedicaldomain
AT huangxiaofeng knowledgegraphbasedquestionansweringmethodformedicaldomain
AT zhangjixin knowledgegraphbasedquestionansweringmethodformedicaldomain
AT xuzisang knowledgegraphbasedquestionansweringmethodformedicaldomain
AT oulu knowledgegraphbasedquestionansweringmethodformedicaldomain
AT tongjianbin knowledgegraphbasedquestionansweringmethodformedicaldomain