PMID- 34604514
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211005
IS  - 2376-5992 (Electronic)
IS  - 2376-5992 (Linking)
VI  - 7
DP  - 2021
TI  - A knowledge graph based question answering method for medical domain.
PG  - e667
LID - 10.7717/peerj-cs.667 [doi]
LID - e667
AB  - 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.
CI  - (c) 2021 Huang et al.
FAU - Huang, Xiaofeng
AU  - Huang X
AD  - School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China.
FAU - Zhang, Jixin
AU  - Zhang J
AD  - School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China.
FAU - Xu, Zisang
AU  - Xu Z
AD  - Computer and Communication Engineer Institute, Changsha University of Science and 
      Technology, Changsha, Hunan, China.
FAU - Ou, Lu
AU  - Ou L
AD  - College of Computer Science and Electronic Engineering, Hunan University, 
      Changsha, Hunan, China.
FAU - Tong, Jianbin
AU  - Tong J
AD  - Hunan Province Key Laboratory of Brain Homeostasis, Third Xiangya Hospital, 
      Central South University, Changsha, Hunan, China.
LA  - eng
PT  - Journal Article
DEP - 20210901
PL  - United States
TA  - PeerJ Comput Sci
JT  - PeerJ. Computer science
JID - 101660598
PMC - PMC8444078
OTO - NOTNLM
OT  - Knowledge graph
OT  - Medical domain
OT  - Question answering
OT  - Weighted path ranking
COIS- The authors declare that they have no competing interests.
EDAT- 2021/10/05 06:00
MHDA- 2021/10/05 06:01
PMCR- 2021/09/01
CRDT- 2021/10/04 06:11
PHST- 2020/11/27 00:00 [received]
PHST- 2021/07/19 00:00 [accepted]
PHST- 2021/10/04 06:11 [entrez]
PHST- 2021/10/05 06:00 [pubmed]
PHST- 2021/10/05 06:01 [medline]
PHST- 2021/09/01 00:00 [pmc-release]
AID - cs-667 [pii]
AID - 10.7717/peerj-cs.667 [doi]
PST - epublish
SO  - PeerJ Comput Sci. 2021 Sep 1;7:e667. doi: 10.7717/peerj-cs.667. eCollection 2021.