Understanding the domain of driving distraction with knowledge graphs

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs wer...

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Vydané v:PloS one Ročník 17; číslo 12; s. e0278822
Hlavní autori: Xu, Wenxia, Feng, Lei, Ma, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 09.12.2022
Public Library of Science (PLoS)
Predmet:
Algorithms
Automobile Driving
Bibliographic coupling
Bibliometrics
Computational linguistics
Computer and Information Sciences
Content analysis
Data mining
Data science
Deep learning
Engineering and Technology
Evaluation
Graphs
Human factors
Humans
Keywords
Knowledge
Knowledge representation
Language processing
Medical research
Medicine and Health Sciences
Methods
Natural language interfaces
Natural Language Processing
Physical Sciences
Programming languages
Research and Analysis Methods
Research Design
Research methodology
Research methods
Statistics
Traffic
Traffic accidents
Visualization
China
United States > US
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0278822