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Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning
- Source :
- Applied Sciences, Vol 9, Iss 13, p 2720 (2019), Applied Sciences, Volume 9, Issue 13
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration<br />therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.
- Subjects :
- 0209 industrial biotechnology
Industry 4.0
Computer science
02 engineering and technology
lcsh:Technology
Field (computer science)
lcsh:Chemistry
020901 industrial engineering & automation
Industrial technology
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Intellectualization
industry 4.0
Instrumentation
lcsh:QH301-705.5
Fluid Flow and Transfer Processes
Social network
business.industry
lcsh:T
Process Chemistry and Technology
Deep learning
General Engineering
intellectualization of industrial information
deep learning
020207 software engineering
Construct (python library)
industrial big data
Data science
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
social network
Artificial intelligence
business
Intelligent control
lcsh:Engineering (General). Civil engineering (General)
industrial knowledge graph
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 9
- Issue :
- 13
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....a046776877235f46be601b1c94774b63