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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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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 |
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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 |
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